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Interview Preparation Guide ​

Senior Site Reliability Engineer I - Axon Enterprise ​

Location: Remote (Australia)
Prepared: November 2025

About Axon ​

Mission: Protect Life

Company Overview:

  • Public safety technology company focused on law enforcement and security solutions
  • Strong growth trajectory: 31% YoY revenue growth, reaching $711M in Q3 2025
  • Software & Services growing 41%, with $1.3B in annual recurring revenue
  • Known for TASER devices, body cameras, digital evidence management, and real-time operations software
  • Recently acquired Prepared and Carbyne to enhance AI-powered emergency response capabilities

Company Culture & Values:

  • Mission-Driven: "Protect Life" - focus on societal safety and justice
  • Aim Far: Think big with a long-term view to reinvent the world to be safer
  • Collaboration: Work better together, connect with candor and care
  • Fast-Paced & Impactful: Meaningful work with opportunities to drive real change
  • Inclusion & Diversity: Committed to building teams that reflect communities served
  • Employee Well-being: Competitive compensation, PTO, enhanced parental leave, wellness support

Technical Depth Questions ​

1. How would you design a foundational platform that enables engineering teams to provision services rapidly, consistently, and securely? ​

Situation: At Domain, I was tasked with designing and building a Kubernetes-based platform to replace our aging ECS infrastructure, enabling 20+ product teams to deploy independently and securely.

Task: My objective was to create a self-service platform aligned with my vision of nurturing a reliability culture—providing user-friendly tools that enable teams to deliver efficient services with predictable resilience while reducing deployment complexity and risk.

Actions:

  1. Conducted Discovery Workshops: Ran workshops and surveys with product teams to identify their biggest pain points. Found that 40% of deployment delays stemmed from manual approval gates, tight CI/CD coupling, and CloudFormation dependencies.

  2. Defined Current and Future States: Mapped the existing architecture and designed a phased transformation approach:

    • Short-term: Kubernetes platform with GitOps workflows
    • Long-term: Self-service with standardized templates and automated security/quality gates

  3. Built Standardized Infrastructure:

    • Created Helm charts to standardize environment templates, eliminating configuration drift
    • Developed ArgoCD-based GitOps workflows with automated canary deployments
    • Integrated security tooling (Orca, CrowdStrike) and quality gates (SonarQube) directly into pipelines
    • Implemented centralized observability (ELK stack) for all services by default

  4. Enabled Self-Service & Fostered Reliability Culture:

    • Created comprehensive documentation and video tutorials to build team expertise
    • Held bi-weekly platform hours for real-time troubleshooting and knowledge sharing, fostering collaborative learning
    • Introduced feature flags to decouple release from deployment, allowing teams to deploy frequently while controlling releases
    • Embedded operational excellence by making reliability and observability self-service capabilities, not afterthoughts

  5. Phased Migration Strategy:

    • Phase 1: Gateway migration (APIs routed from DNS to K8s Gateway, then back to ECS) with value-add functions like authentication and rate limiting
    • Phase 2: Migrated APIs and services from ECS to K8s with existing CI pipeline and new ArgoCD deployment pipeline
    • Phase 3: Migrated Jenkins CI steps to GitHub Actions

Result:

  • Enabled 20+ product teams to deploy independently with zero manual intervention
  • Deployment frequency increased from 2/week to 5/day
  • Mean deployment time dropped to 9 minutes
  • Security vulnerabilities caught early in the pipeline workflow
  • More than 50% of workloads migrated within 7 months
  • Post-migration Mean Time to Recovery reduced to ~15 minutes
  • Overall AWS costs reduced by 18% after removing migrated ECS stacks
  • 4.7/5 developer satisfaction score (n=76 respondents)

2. Describe your experience with Kubernetes platforms like AKS or EKS. How have you optimized them for reliability and scalability? ​

Situation: At Domain, we were running 100+ microservices on AWS ECS, but the platform had reliability issues, lacked standardization, and made it difficult for teams to achieve their deployment velocity goals. We needed to migrate to a more scalable and reliable Kubernetes environment.

Task: My responsibility was to design, build, and operate a production-grade Kubernetes platform that would support 100+ microservices while maintaining 99.9% uptime and enabling teams to deploy independently.

Actions:

  1. Platform Design:

    • Architected a Kubernetes environment on AWS using EKS
    • Implemented Infrastructure as Code using AWS CDK and Terraform for consistency and repeatability
    • Designed for multi-AZ high availability with automated failover

  2. Reliability & Observability:

    • Achieved 40% SLO adoption rate in 3 months by designing and implementing SLO Helm charts for general applications
    • Maintained 99.9% uptime for core infrastructure serving Domain's production workloads through comprehensive monitoring and automation
    • Integrated centralized logging (ELK stack) with alerting for anomalies
    • Added custom instrumentation to critical code paths for deep observability

  3. Standardization & Self-Service:

    • Created Helm charts to standardize environment templates
    • Built standardized workflows for deployment, scaling, and rollback
    • Developed runbooks and checklists linked to alerts for faster incident resolution

  4. Security & Compliance:

    • Shifted security left by integrating Orca and CrowdStrike into platform pipelines
    • Embedded quality gates with SonarQube integration
    • Implemented least-privilege IAM policies and network security policies

  5. Developer Experience:

    • Integrated ArgoCD for GitOps-based deployments
    • Migrated 15+ teams to GitOps workflows, upgrading CI/CD tooling across Jenkins, GitHub Actions, and Argo CD
    • Created self-service documentation to accelerate adoption

Result:

  • Successfully migrated from ECS to Kubernetes supporting 100+ microservices
  • Maintained 99.9% uptime throughout migration and operation
  • 40% SLO adoption rate within 3 months
  • Platform enabled independent deployment for 20+ product teams
  • Security and quality checks embedded into every deployment
  • Rollback capabilities through GitOps made recovery seamless

3. Tell me about a time you debugged a complex issue in a cloud-native distributed system. What was your approach? ​

Situation: At Domain, during a Saturday afternoon (peak auction results delivery time), we experienced a cascading failure that caused 26 minutes of downtime. I was on-call as the platform team incident responder when the alert fired.

Task: My goal was to quickly identify the root cause, mitigate the incident, and restore service to normal operation while coordinating with the application team.

Actions:

  1. Initial Assessment:

    • Joined the incident meeting and discovered there was no existing troubleshooting checklist or runbook for this service
    • Started with the alert itself, which showed message count in the queue system exceeded threshold

  2. Log Analysis:

    • Extracted logs from ELK stack for the affected service and timeframe
    • Analyzed patterns and discovered more than 90% of logs contained HTTP 429 (rate limit) errors to a 3rd-party service API endpoint

  3. Root Cause Identification:

    • Determined our service was not respecting 429 responses and kept retrying without backoff period
    • Checked with developers and confirmed the issue was caused by the latest code change
    • Verified the previous version was backwards compatible

  4. Mitigation:

    • Executed rollback to restore service to working state
    • Monitored system recovery and confirmed normal operation

  5. Post-Incident Improvements:

    • Created a detailed incident timeline and root cause analysis
    • Worked with team to establish action items:
      • Wrote clear troubleshooting checklist for the service
      • Linked checklist to relevant alerts for future incidents
      • Created backlog ticket for proper 429 handling with exponential backoff

Result:

  • Service restored within acceptable timeframe
  • Created reusable troubleshooting artifacts to reduce MTTR for future incidents
  • Improved incident response process with actionable checklists
  • Bug properly tracked and prioritized for permanent fix

4. How do you approach Infrastructure as Code? What patterns and best practices do you follow? ​

Situation: At illion, our SOC 2 audit revealed that our AWS environment had security vulnerabilities due to manual infrastructure changes, inconsistent configurations, and lack of audit trails. We needed to achieve compliance while not slowing down development.

Task: I led the initiative to migrate our entire AWS environment to Infrastructure as Code using Terraform, enforce security compliance, and automate infrastructure provisioning without adding overhead to developers.

Actions:

  1. IaC Strategy & Tooling:

    • Chose Terraform for its declarative approach, mature AWS provider, and strong community support
    • Structured modules for reusability: network modules, compute modules, security modules
    • Implemented version control with Git and code review processes via pull requests

  2. Security by Default:

    • Created Terraform modules that enforced security rules and least-privilege IAM policies
    • Integrated tfsec checks into CI/CD pipeline to catch security issues before deployment
    • Designed modules with security best practices baked in (encryption at rest/transit, private subnets, security groups)

  3. Automation & Compliance:

    • Built Packer pipeline integrated with AWS Secrets Manager to generate ephemeral AMIs refreshed weekly or when CVE triggered
    • Automated infrastructure provisioning through CI/CD, reducing human error
    • Created comprehensive audit trail through version control and Terraform state management

  4. Developer Experience:

    • Developed clear documentation and examples for using modules
    • Established contribution guidelines for expanding modules
    • Created wrapper scripts to simplify common operations

  5. Testing & Validation:

    • Implemented automated testing of infrastructure changes
    • Used Terraform plan in CI to show impact before applying
    • Maintained separate state files for different environments

Result:

  • Achieved SOC 2 Type 2 and ISO 27001 compliance with zero critical findings
  • Entire AWS environment migrated to Infrastructure as Code
  • Reduced security review time from 48 hours to 15 minutes
  • Eliminated 92% of runtime vulnerabilities related to outdated dependencies
  • Improved production update efficiency by 50% and reduced human dependencies by 90%

5. What's your experience with observability tools? How have you used them to improve system reliability? ​

Situation: At Envato, we had high observability costs with New Relic (~$150K/year), alert fatigue from too many non-actionable alerts, and slow incident resolution times because engineers couldn't quickly correlate data across different monitoring tools.

Task: I was tasked with reducing observability costs by at least 30% while advancing our operational excellence through better monitoring and observability—creating a culture where teams could understand system behavior, make data-driven decisions, and resolve incidents faster.

Actions:

  1. Tool Evaluation & Migration:

    • Evaluated Datadog as a replacement for New Relic based on cost, features, and integration capabilities
    • Designed migration strategy to minimize disruption to on-call engineers
    • Migrated from New Relic to Datadog, consolidating APM, logging, and infrastructure monitoring into one platform

  2. Alert Optimization:

    • Audited all existing alerts and classified them by actionability and business impact
    • Reduced meaningless alerts by 60%, ensuring remaining alerts all had clear action items
    • Enforced that every alert must have an associated runbook

  3. Incident Response Integration:

    • Integrated PagerDuty workflows with Datadog for automated escalation
    • Created correlation dashboards showing relationships between metrics, logs, and traces
    • Enabled data-driven decision-making during incidents with pre-built dashboards

  4. Custom Instrumentation:

    • Added custom OpenTelemetry instrumentation to critical code paths in Ruby and Node.js applications
    • Implemented distributed tracing for complex workflows spanning multiple services
    • Created business-level metrics alongside technical metrics

  5. Built Culture & Expertise:

    • Trained engineers on effective use of observability tools, fostering a culture of data-driven decision-making
    • Created runbooks for common troubleshooting scenarios to share expertise across teams
    • Established on-call procedures tied to specific alerts, ensuring consistent and productive observability practices

Result:

  • Reduced observability costs by 45% (from ~$150K to ~$82K annually)
  • Improved incident resolution time by 40%
  • Eliminated alert fatigue through focused, actionable alerts
  • Enhanced application troubleshooting through custom instrumentation
  • Enabled faster root cause analysis through integrated APM, logging, and tracing

6. How do you balance rapid feature development with system stability and operational excellence? ​

Situation: At Domain, I was assigned to uplift the overall operational capability of a software product team within 6 months. This included improving branching strategy, CI/CD pipeline, testing, observability, and incident management—all while the team needed to continue delivering features.

Task: I needed to prioritize improvements based on business impact while ensuring the team could continue feature development with minimal disruption. My focus was achieving operational excellence through systematic improvements in reliability, resilience, observability, and team culture.

Actions:

  1. Stakeholder Alignment:

    • Ran a workshop with stakeholders to understand priorities and concerns
    • Carefully weighed pros and cons of each improvement area considering long-term impact
    • Secured agreement on phased approach that wouldn't block feature delivery

  2. Prioritization & Planning:

    • Built high-level design of future state
    • Designed migration approach allowing developers to continue work during transformation
    • Prioritized: branching strategy, CI/CD, and observability over testing (which could follow later)

  3. Incremental Implementation:

    • Months 1-3: Changed branching strategy to trunk-based development, migrated one service to ECS with standard pipeline and testing as proof of concept
    • Months 3-4: Created detailed migration documentation, enabling the software team to migrate remaining 5 services with minimal support from me
    • Months 4-6: Added observability to newly migrated services, completed troubleshooting guides and runbooks
    • Final 2 weeks: Ran incident management training sessions with team

  4. Risk Management Through Operational Excellence:

    • Used error budgets and SLO framework to make data-driven decisions about release timing—embedding reliability culture into day-to-day decisions
    • In one instance, pushed back on adding two new features when error budget analysis showed only 5 minutes remaining in 30-day SLA window
    • Presented data on customer complaints to prioritize critical bug fixes over new features, balancing velocity with predictable resilience

Result:

  • Successfully uplifted 4 out of 5 areas (deferred testing to later phase)
  • Team continued feature delivery throughout the 6-month transformation
  • Increased customer satisfaction and reduced escalations
  • More reliable product with clearer operational procedures
  • Team empowered to operate and maintain their own services

Behavioral & Leadership Questions ​

7. Tell me about a time you influenced and educated the engineering organization to adopt new architectural patterns or practices. ​

Situation: During Domain's migration from ECS to Kubernetes, several product teams resisted adopting the new platform due to perceived operational complexity and concerns about learning curve. Our platform engineering team needed widespread adoption to justify the investment and realize benefits.

Task: We needed to achieve 50% adoption of the Kubernetes platform within nine months while maintaining feature delivery timelines and building engineer confidence in the new system.

Actions:

  1. Phased Onboarding Strategy:

    • Designed a three-phase migration with minimal customer involvement and comprehensive metric collection:
      • Phase 1: Gateway migration for APIs to route traffic from DNS to K8s Gateway, then back to ECS load balancer. Added value-add functions like authentication and rate limiting to demonstrate immediate benefits
      • Phase 2: Migrated APIs and services from ECS to K8s while keeping existing CI pipeline, adding new ArgoCD deployment pipeline for gradual transition
      • Phase 3: Migrated Jenkins CI steps to GitHub Actions for full modernization

  2. Education & Support:

    • Held bi-weekly platform hours to share current state, roadmap, and offer FAQ sessions with real-time troubleshooting
    • Created self-service documentation and video tutorials tailored to different team skill levels
    • Paired with engineers from adopting teams to provide hands-on guidance

  3. Demonstrating Value:

    • Started with gateway migration showing immediate security and performance benefits
    • Shared success metrics from early adopters with broader organization
    • Highlighted reduced deployment times and improved reliability in migration showcase sessions

  4. Building Confidence:

    • Maintained detailed troubleshooting guides and runbooks
    • Ensured rollback procedures were well-documented and tested
    • Created Slack channel for quick support and community knowledge sharing

Result:

  • More than 50% of workloads migrated within 7 months (ahead of 9-month target)
  • Post-migration Mean Time to Recovery improved to ~15 minutes
  • Received 4.7/5 in post-migration developer satisfaction survey (n=76 respondents)
  • Overall AWS cost reduced by 18% in final month after removing migrated ECS stacks
  • Platform adoption became self-sustaining as teams saw benefits and recommended it to peers

8. Describe a time when you had to make a technical decision with incomplete information. How did you approach it? ​

Situation: At Domain, I needed to evaluate the best infrastructure-as-code solution to deliver to our developers. The initial choice seemed straightforward—Terraform was already being used and had good community support—but I wanted to ensure it was the right choice for our teams.

Task: I needed to make a decision on our IaC tooling that would impact how all engineering teams interact with infrastructure, but I had limited time for evaluation and incomplete information about all teams' preferences and capabilities.

Actions:

  1. Structured Evaluation:

    • Evaluated Terraform from multiple perspectives: customizability, modularization, integration, maintainability, and community support
    • Gathered initial positive feedback from engineers who had created PRs in our Terraform infrastructure repo

  2. Went the Extra Mile:

    • Rather than stopping at positive feedback, decided to interview engineers across the broader organization
    • Discovered surprising insight: many engineers were not comfortable with YAML/HCL and were unwilling to contribute to Terraform modules
    • This would create dependency on platform team and reduce self-service capability

  3. Customer-Centric Thinking:

    • Shifted perspective to think from developers' viewpoint—our true customers
    • Realized that AWS CDK using TypeScript would be more familiar to our engineering organization
    • TypeScript was already widely used across product teams, reducing learning curve

  4. Made Decision with Available Data:

    • Even though CDK was newer with smaller community, prioritized developer experience and adoption potential
    • Decided to build CDK wrapper/constructs instead of Terraform modules
    • Accepted trade-off of smaller community for better internal adoption

Result:

  • CDK/Constructs proved to be more accessible solution for our engineering organization
  • Higher contribution rate from product teams to infrastructure code
  • Reduced bottleneck on platform team
  • Better alignment with existing tech stack and skills

9. Tell me about a time you had to disagree and commit. How did you handle it? ​

Situation: At Domain, our engineering manager introduced pair programming as a new practice for our platform engineering team. Despite proven benefits in the industry, most engineers—including me—were hesitant about adopting it. Many expressed concerns about reduced individual productivity, uncomfortable collaboration, and perception that having two engineers on one task would slow down our Kubernetes migration project.

Task: As a Senior DevOps Engineer responsible for building "platforms which are easy to use, learn, deploy and contribute to," I needed to balance my initial concerns with openness to practices that could improve our developer experience and platform reliability. I had to find a constructive approach despite my skepticism.

Actions:

  1. Deep Research:

    • Rather than continuing resistance, researched pair programming thoroughly
    • Studied Martin Fowler's comprehensive article on different pairing techniques
    • Learned about various approaches: Ping Pong pairing (test-driven), Strong-Style pairing (knowledge transfer), and Pair Development (collaborative mindset)

  2. Proposed Structured Trial:

    • Suggested a four-week trial to manager and team to experiment with different pairing styles for specific tasks
    • For ArgoCD pipeline work, recommended Ping Pong approach to improve test coverage
    • For onboarding newer team members to Kubernetes, suggested Strong-Style pairing for knowledge transfer

  3. Led by Example:

    • Volunteered to be first participant, pairing with both senior and junior engineers on different platform components
    • Created simple framework to document experiences
    • Tracked metrics like defect rates, knowledge dissemination, and engineer satisfaction

  4. Created Feedback Loop:

    • Facilitated weekly retrospectives specifically about pairing experiences
    • Created safe space for honest feedback about what worked and what didn't
    • Demonstrated commitment to objectively evaluating practice rather than rejecting it

Result:

  • Trial revealed significant benefits: 40% reduction in critical bugs in paired code, faster knowledge transfer to new team members, and improved collaboration
  • After positive results, I became advocate for pair programming and helped formalize guidelines for broader organization
  • Learned to channel my inclination for thoroughness more strategically
  • Demonstrated that systematic testing of new practices despite initial skepticism leads to continuous improvement

10. How do you ensure your solutions are empathetic to the needs of software engineers? ​

Situation: At Domain, I observed that our platform engineering team was building tools and infrastructure with good technical foundations, but adoption was slower than expected. Through conversations, I realized we were solving problems from our perspective rather than truly understanding developer pain points.

Task: I needed to shift our approach to be more customer-centric, treating product engineers as our primary customers. This aligns with my mission that creating reliable user experiences is as much a cultural concern as technological—I needed to provide both expertise and guidance, not just tooling.

Actions:

  1. Direct Engagement:

    • Conducted workshops and surveys with product teams before building solutions
    • Attended their stand-ups and sprint planning sessions to understand daily challenges
    • Asked open-ended questions: "What's the most frustrating part of your deployment process?" rather than "Would you use this feature?"

  2. Validated Assumptions:

    • When evaluating Infrastructure as Code tooling, didn't stop at positive feedback from a few engineers
    • Went the extra mile to talk with engineers across the organization
    • Discovered many were not comfortable with Terraform/YAML, leading to decision to use AWS CDK with TypeScript instead

  3. Built for Self-Service:

    • Created comprehensive documentation and video tutorials at different skill levels
    • Designed platforms to be "easy to use, learn, deploy, contribute to, and operate"
    • Provided multiple support channels: written docs, videos, office hours, Slack support

  4. Measured Success from Developer Perspective:

    • Tracked adoption metrics and satisfaction scores
    • Conducted post-migration surveys to gather feedback (achieved 4.7/5 satisfaction)
    • Used feedback to continuously improve platform and documentation

  5. Created Feedback Loops & Built Expertise:

    • Held bi-weekly platform hours for real-time Q&A and troubleshooting, fostering collaborative learning
    • Maintained public roadmap showing upcoming features based on team requests
    • Established clear process for teams to request features or report issues
    • Built a culture where teams had the expertise and confidence to contribute back to the platform

Result:

  • Platform adoption increased significantly when solutions matched developer workflows
  • 4.7/5 developer satisfaction score in post-migration survey (n=76 respondents)
  • Higher contribution rate to platform tools when using familiar technologies (TypeScript vs HCL)
  • Reduced dependency on platform team through effective self-service capabilities

11. Tell me about a time you took ownership of a problem outside your immediate responsibilities. ​

Situation: At Domain, we had intermittent outages at night for a few days. The site stopped responding for a few minutes and recovered automatically. On the third day, I looked at the on-call channel and noticed engineers were spending significant time jumping between different dashboards trying to diagnose the issue. While I wasn't directly responsible for that service, I recognized an opportunity to improve our incident response process.

Task: Although another SRE was already included in the incident team, I thought it was best to improve our on-call/troubleshooting process immediately rather than wait for a formal retrospective.

Actions:

  1. Jumped In Proactively:

    • Observed what engineers were looking at and identified patterns in their troubleshooting approach
    • Recognized this was a systemic problem affecting incident response across teams

  2. Created Immediate Improvements:

    • Wrote a troubleshooting checklist with steps to find impact, timeline, and service health
    • Created pre-triage dashboard for time correlations across systems
    • Built core vitals dashboard for quick service health assessment

  3. Made It Reusable:

    • Linked checklists directly to relevant alerts for future incidents
    • Documented the approach so other teams could replicate
    • Shared learnings in team meeting to improve organization-wide practices

Result:

  • Created a standardized process for handling incidents during on-call
  • Reduced time to triage issues by providing clear starting point
  • Dashboards and checklists adopted by other teams
  • Improved Mean Time to Recovery organization-wide

12. How do you approach learning new technologies and staying current in a fast-moving field? ​

Situation: Throughout my career, I've consistently needed to learn new technologies quickly—from migrating ECS to Kubernetes at Domain, to implementing new observability tools at Envato, to working with emerging AI capabilities at Viator.

Task: I need to stay current with rapidly evolving cloud-native technologies while delivering on current responsibilities and ensuring learning translates to practical value for my organization.

Actions:

  1. Learning by Doing:

    • When researching pair programming at Domain despite initial resistance, I studied Martin Fowler's comprehensive article and immediately proposed a structured four-week trial
    • Rather than theoretical learning, volunteered to be first participant to gain hands-on experience

  2. Research Before Implementation:

    • When working with technologies, I leverage official documentation and community best practices
    • Read release notes and changelogs to understand new features and improvements
    • Follow technology leaders and practitioners on technical blogs and forums

  3. Community Engagement:

    • Participate in technical communities to learn from others' experiences
    • Share learnings through documentation and knowledge-sharing sessions
    • Attend conferences and webinars when relevant to current challenges

  4. Structured Evaluation:

    • When evaluating new tools (e.g., Terraform vs CDK, New Relic vs Datadog), create evaluation criteria: customizability, integration, maintainability, community support
    • Gather feedback from actual users beyond marketing materials
    • Run proof-of-concept implementations before full commitment

  5. Knowledge Sharing:

    • Create documentation and video tutorials for new technologies adopted
    • Hold platform hours and training sessions to share knowledge with teams
    • Build reusable patterns and examples that others can learn from

Result:

  • Successfully adopted and implemented new technologies across multiple organizations
  • Reduced learning curve for teams through effective knowledge sharing
  • Made informed technology decisions based on practical evaluation rather than hype

Culture & Communication Challenges ​

13. Tell me about a time you had to align multiple teams around a change they initially resisted. ​

Situation: During Domain's migration from ECS to Kubernetes, several product teams resisted adopting the new platform due to perceived operational complexity and concerns about learning curve. Our platform engineering team needed widespread adoption to justify the investment and realize benefits.

Task: Achieve broad adoption of 50% within nine months while maintaining feature velocity. This aligned with my vision of nurturing a reliability culture through approachable, self-service platforms—recognizing that creating reliable experiences is as much a cultural concern as technological.

Actions:

  1. Listened and Mapped Readiness:

    • Ran discovery workshops to hear concerns directly from teams
    • Mapped readiness levels by team to understand where resistance came from
    • Identified that fear of complexity and slowed delivery were primary blockers

  2. Designed Phased Approach with Quick Wins:

    • Phase 1: Gateway migration routing DNS → K8s Gateway → back to ECS with immediate value-add (authentication, rate limiting)
    • Phase 2: Kept existing CI pipeline, introduced ArgoCD for gradual GitOps adoption
    • Phase 3: Moved Jenkins steps to GitHub Actions for full modernization
    • This reduced perceived risk and showed value without forcing full migration upfront

  3. Over-Communicated and Built Expertise:

    • Bi-weekly platform hours for FAQ sessions and real-time troubleshooting
    • Created Slack channel for quick support and community knowledge sharing
    • Developed self-service docs and video tutorials tailored to different skill levels
    • Paired with engineers from adopting teams for hands-on guidance
    • Maintained public roadmap and ran migration showcases sharing wins and lessons

  4. Reduced Perceived Risk:

    • Tested and documented rollback procedures
    • Maintained detailed runbooks for common scenarios
    • Showed that teams could move at their own pace within the phased framework

Result:

  • More than 50% of workloads migrated within 7 months (ahead of 9-month target)
  • Maintained 99.9% uptime throughout migration
  • Post-migration MTTR improved to ~15 minutes
  • Overall AWS costs reduced by 18%
  • 4.7/5 developer satisfaction score (n=76 respondents)
  • Platform adoption became self-sustaining as teams advocated for it to peers

14. Describe a time you matured operational practices where processes were immature and change was sensitive. ​

Situation: At Envato, observability was fragmented across multiple tools (New Relic and others), alert fatigue was high with many non-actionable alerts, and incident resolution was slow because engineers couldn't quickly correlate data.

Task: Reduce observability costs by at least 30% while driving operational excellence. My focus was making reliability and observability cultural habits embedded in team workflows, not just tools teams were forced to use.

Actions:

  1. Co-Created Shared Standards:

    • Worked with teams to define "actionable alert" criteria together
    • Required every alert to have an associated runbook—no exceptions
    • Built consensus that this improved on-call quality of life, not just metrics

  2. Consolidated and Streamlined Tooling:

    • Audited all existing alerts and removed 60% of noise
    • Migrated from New Relic to Datadog, consolidating APM, logging, and infrastructure monitoring
    • Integrated PagerDuty with clear escalation paths and correlation dashboards

  3. Built Culture and Expertise:

    • Rolled out training sessions and office hours on effective observability use
    • Published runbooks and troubleshooting examples for common scenarios
    • Established on-call procedures tied to specific alerts
    • Fostered data-driven decision-making culture through accessible dashboards

  4. Framed Around Empathy:

    • Positioned initiative as improving developer sustainability and reducing burnout
    • Emphasized fewer pages, more meaningful signals, faster resolution
    • Made teams partners in the improvement, not recipients of mandates

Result:

  • Observability costs reduced 45% (from ~$150K to ~$82K annually)
  • Alert noise reduced 60%, eliminating alert fatigue
  • Incident resolution time improved 40%
  • Teams adopted runbook-first mindset
  • Reliability and observability became self-service practices embedded in workflows

15. Tell me about a time you brought clarity to an ambiguous, cross-cutting decision with incomplete information. ​

Situation: At Domain, choosing organization-wide Infrastructure as Code tooling was contentious. Terraform had momentum and community support, but developer preferences and contribution patterns were unclear. The decision would impact how all engineering teams interact with infrastructure.

Task: Decide on tooling that maximizes adoption and self-service capability. This aligned with my mission that reliable experiences require cultural adoption, not just selecting the "best" technology on paper.

Actions:

  1. Structured Evaluation Framework:

    • Created evaluation criteria: customizability, modularization, integration, maintainability, community support
    • Gathered initial positive feedback from engineers who had contributed to Terraform infrastructure repo

  2. Went Beyond Surface-Level Feedback:

    • Rather than stopping at positive feedback, interviewed engineers across broader organization
    • Discovered surprising insight: many engineers were not comfortable with HCL/YAML and were unwilling to contribute to Terraform modules
    • This would create dependency on platform team and reduce self-service capability

  3. Reframed Success Criteria:

    • Shifted focus from "best tool" to "broad contribution and self-service adoption"
    • Realized AWS CDK using TypeScript aligned with existing organizational skills
    • TypeScript was already widely used across product teams, reducing learning curve

  4. Socialized Decision with Transparency:

    • Created clear decision brief documenting criteria, trade-offs, and rationale
    • Built CDK constructs and documentation to reduce adoption friction
    • Accepted trade-off of smaller community for better internal adoption potential

Result:

  • Higher contribution rate from product teams to infrastructure code
  • Reduced bottleneck on platform team
  • Better alignment with existing tech stack and skills
  • Faster adoption across organization
  • Clarity in decision criteria + empathy for developer workflows led to durable decision

16. Tell me about a time you influenced a culture change beyond just technical solutions. ​

Situation: At Domain, our engineering manager introduced pair programming for the platform engineering team. Despite proven industry benefits, most engineers—including me—were hesitant. Concerns included reduced individual productivity, uncomfortable collaboration, and perception that pairing would slow down our Kubernetes migration project.

Task: Evaluate whether pairing could improve quality and knowledge sharing without jeopardizing delivery. As someone responsible for building platforms that are "easy to use, learn, deploy and contribute to," I needed to model "aim far" thinking while staying pragmatic about team velocity.

Actions:

  1. Research and Propose Structured Trial:

    • Rather than continuing resistance, thoroughly researched pair programming approaches
    • Studied Martin Fowler's comprehensive article on different pairing techniques
    • Proposed four-week trial with explicit goals per task type:
      • Ping-Pong pairing for ArgoCD pipeline work to improve test coverage
      • Strong-Style pairing for Kubernetes onboarding to accelerate expertise building

  2. Led by Example:

    • Volunteered to be first participant, pairing with both senior and junior engineers
    • Created simple framework to document experiences
    • Tracked metrics: defect rates, knowledge dissemination, engineer satisfaction

  3. Created Safe Feedback Loop:

    • Facilitated weekly retrospectives specifically about pairing experiences
    • Created safe space for honest feedback about what worked and what didn't
    • Demonstrated commitment to objectively evaluating practice rather than rejecting it

  4. Documented and Shared:

    • Created guidelines and patterns from learnings
    • Shared outcomes with broader organization
    • Made case based on data, not just philosophy

Result:

  • 40% reduction in critical bugs in paired code
  • Faster knowledge transfer to new team members
  • Improved team collaboration
  • Skepticism shifted to advocacy—I became champion for pair programming
  • Helped formalize pairing guidelines for broader organization
  • Advanced operational excellence through culture and expertise building—no new tools required

17. Why are you interested in Axon, and how does this role align with your career goals? ​

Mission Alignment: I'm drawn to Axon's mission to "Protect Life." Throughout my career, my vision has been to nurture a reliability culture and support feature teams in delivering efficient services with predictable resilience—creating the best customer experience. The opportunity to apply this vision toward societal safety and justice, where reliable user experiences directly impact public safety, is incredibly meaningful.

Technical Challenge & Operational Excellence: The Senior SRE I role aligns perfectly with my experience and focus on operational excellence:

  • Building foundational platforms with reliability and resilience (similar to my K8s platform work at Domain)
  • Advancing observability and monitoring practices to enable data-driven decisions
  • Influencing culture and expertise through collaboration (achieved 50%+ adoption of new platform)
  • Balancing innovation with reliability (maintained 99.9% uptime while driving major migrations)
  • Providing user-friendly tools and expertise to increase productivity and decrease risk

Growth Opportunity: Axon's rapid growth (31% YoY revenue, $2.74B projected) and investment in R&D for new products like Vehicle Intelligence, Axon Body Workforce Mini, and AI-powered emergency communications platforms represents an exciting environment. The scale and impact of supporting mission-critical systems used by emergency responders aligns with my goal to work on systems where reliability truly matters.

Company Culture: Axon's values resonate with my work style:

  • Aim Far: I've consistently driven long-term transformation (6-month platform uplifts, multi-year K8s migrations)
  • Collaboration: I prioritize working with teams through workshops, platform hours, and pair programming
  • Fast-Paced & Impactful: I thrive in environments where I can take ownership and drive meaningful change
  • Continuous Learning: I'm constantly exploring new technologies and patterns to improve developer experience

Australian Presence: Working remotely from Australia for a company with strong global presence while contributing to products with real-world impact on safety and justice is an ideal combination.

Career Trajectory: This role positions me to deepen my expertise in cloud-native SRE practices while expanding into new domains (public safety technology) and contributing to a mission-driven organization at significant scale.

Questions to Ask Axon ​

Technical & Platform ​

  1. What's the current state of your cloud infrastructure? Are you primarily on Azure, AWS, or multi-cloud?
  2. What does your Kubernetes platform look like? AKS? What's the scale (number of clusters, services, deployments per day)?
  3. What's your approach to observability? Which tools are you using for APM, logging, and metrics?
  4. How mature is your GitOps/CI/CD practice? What does the deployment pipeline look like?
  5. What are the biggest operational challenges the SRE team is currently facing?

Team & Culture ​

  1. How is the SRE team structured? How many SREs are there, and how do they collaborate with product teams?
  2. What does the on-call rotation look like? What's the average incident load?
  3. How does the team balance operational work with platform development and innovation?
  4. What does success look like for this role in the first 6 months? First year?
  5. How does Axon support continuous learning and professional development for engineers?

Mission & Impact ​

  1. How do SRE practices directly support Axon's mission to "Protect Life"?
  2. Can you share examples of how platform reliability has impacted customer outcomes?
  3. What recent acquisitions (Prepared, Carbyne) mean for the engineering organization and infrastructure needs?
  4. How does the Australian security clearance requirement affect the work and collaboration?

Growth & Innovation ​

  1. With 31% YoY growth and expanding product lines, how is the infrastructure scaling to meet demand?
  2. What new products or capabilities are on the roadmap that will require SRE support?
  3. How does Axon approach technical debt vs. new feature development?
  4. What's the company's philosophy on build vs. buy for infrastructure tooling?

Key Talking Points ​

Relevant Experience Highlights ​

  • 13 years platform/infrastructure engineering experience
  • Kubernetes expertise: Migrated 100+ microservices from ECS to K8s at Domain
  • Cloud platforms: Deep AWS experience, familiar with multi-cloud patterns
  • Observability: Implemented comprehensive monitoring with ELK, Datadog, Prometheus
  • CI/CD: Extensive experience with GitLab, GitHub Actions, Argo CD, Jenkins
  • IaC: Terraform, AWS CDK, proven track record automating infrastructure
  • Python/TypeScript: Proficient in scripting and automation

Demonstrated Impact ​

  • Maintained 99.9% uptime for production platforms serving critical workloads
  • Achieved 40% SLO adoption in 3 months through thoughtful design
  • Reduced deployment time from 45 minutes to 9 minutes with GitOps
  • Improved incident resolution time by 40% through better observability
  • Enabled 20+ product teams to deploy independently through platform work

Cultural Fit ​

  • Mission-driven: Align technical work with business outcomes and user impact
  • Collaborative: Extensive experience working with cross-functional teams
  • Self-starter: Track record of identifying problems and driving solutions proactively
  • Empathetic: Focus on developer experience and building platforms that are easy to use
  • Continuous learner: Consistently adopt new technologies and share knowledge

Preparation Notes ​

Technical Areas to Refresh ​

  • Azure-specific services (if they're primarily Azure vs AWS)
  • Latest Kubernetes best practices and features
  • Security clearance requirements and implications
  • Public safety technology domain context

Potential Concerns to Address ​

  • Azure experience: While deep AWS experience, highlight transferable cloud-native patterns and quick learning ability
  • Public safety domain: Demonstrate interest in mission and ability to learn domain-specific requirements
  • Security clearance: Confirm Australian citizenship and willingness to obtain required clearance

Success Metrics to Emphasize ​

  • Platform reliability (99.9% uptime)
  • Developer satisfaction (4.7/5 scores)
  • Adoption rates (50%+ migration in 7 months)
  • Cost optimization (18% reduction)
  • Speed improvements (deployment time, incident resolution)
  • Security compliance (SOC 2, ISO 27001)