1. "Tell me about a time you had to solve a complex infrastructure problem"

Situation: At Domain, our architecture for more than 100 services was inefficient and risky. We were running a separate ALB and ECS cluster for every service, which drove up costs significantly. Operationally, deployments were coupled to the CI pipeline—meaning a rollback required a full build re-run (taking from 5 minutes to 30 minutes)—and services were communicating via public endpoints, creating a security surface we needed to close.

Task: I led the strategy to consolidate onto a shared Kubernetes platform. My goals were to reduce infrastructure costs, enable instant GitOps rollbacks (decoupling deployment from release), and build secure internal routing.

Action:

1. I designed a multi-tenant K8s platform to replace the "cluster-per-service" model. Recognizing that density would increase, I proactively re-architected the VPC network (moving from /16 to /8 with /20 subnets) to prevent IP exhaustion before it happened.
2. I selected Argo CD to decouple deployment from release. This shifted us to a GitOps model where rollbacks became instant configuration reverts rather than slow build processes.
3. I led the migration squad to implement internal routing via K8s Gateway API and used a progressive pattern with weighted DNS to migrate services with zero downtime.

Result: We consolidated infrastructure (reducing ALB costs), achieved zero incident during migration, and platform uptime hit 99.9%. The move to GitOps empowered 20+ teams to self-deploy safely, and we successfully enabled service-to-service traffic behind the firewall.

2. "What is the most challenging part of your role?"

Situation: The hardest part of the role is ruthless prioritization—choosing what not to do. In my first quarter at Viator, I identified two potential initiatives: automating the GitLab token rotation process(a known annoyance) versus improving a production-to-staging database refresh pipeline (a complex infrastructure gap).

Task: My goal was to maximize "Engineering Velocity" per hour invested. I needed to look beyond the immediate complaints and identify which initiative would be more aligned with our missions and goals.

Action:

1. Strategy & Alignment: I evaluated the token rotation. While vocalized often, it only cost us ~1 hour/week. Time to refactor to full automation would take 4 weeks. The ROI was low. I made the hard call to not automate it fully. Instead, I delivered a "good enough" script and documentation in 2 days, solving 80% of the pain with 10% of the effort.
2. Force Multiplication: I pivoted my focus to the Database Refresh initiative. The existing 3-day restore process meant mid-week data changes were missing for Monday testing, causing frequent automation failures and developer frustration.
3. Architecture & Complexity: I re-architected the pipeline to decouple the sanitization process from the import process. I implemented a strategy to exclude high-volume unused tables and sanitize PII in parallel, ensuring compliance while optimizing for speed.

Result: By de-prioritizing the low-leverage task, I delivered the Database Refresh pipeline in the same quarter. This reduced the restore time from 3 days to under 4 hours, enabling daily refreshes instead of bi-weekly. This eliminated "stale data" and restored confidence in our automated testing suites.

3. Tell me about a time you had to persuade engineers to adopt new things

Situation: We needed to migrate 100+ services from ECS to Kubernetes to solve scaling and cost issues. However, product teams were fiercely resistant, fearing that "learning Kubernetes" would kill their feature velocity. We were at a stalemate: Platform wanted stability, Product wanted speed.

Task: I realized I couldn't just "convince" them with slides. I had to engineer away the friction. My goal was to make the migration invisible and the value immediate, effectively "selling" the platform by making it the path of least resistance.

Action:

1. Strategy (The "Strangler Fig" Pattern): I architected a phased migration that delivered value before asking for effort.
   • Phase 1 (Zero-Touch Value): I deployed the K8s Gateway API in front of the legacy ECS services. This immediately gave teams free rate-limiting and better authentication without them changing a line of code. This built trust and "wins" early.
2. Force Multiplication (The "Golden Path"): I recognized that writing K8s manifests was the blocker. I built a "Golden Path" abstraction using Helm and ArgoCD. Teams didn't need to learn kubectl; they just had to add a 10-line value file. I turned a complex migration into a simple configuration change.
3. Execution: I led "Office Hours" not just to talk, but to do live migrations. We proved that a service could be migrated in <1 hour, debunking the "it takes too long" myth.

Result:

• Adoption: We hit 50% adoption in 7 months (2 months ahead of schedule).
• Velocity: Post-migration MTTR dropped to 15 minutes (from hours).
• Culture: Developer satisfaction hit 4.7/5 because we didn't just "force" a tool; we solved their deployment pain.
• Impact: Reduced AWS compute costs by 18% by consolidating services efficiently.

4. Tell me about a time when you had a disagreement with your manager (Bias for Action)

Situation: My manager mandated "Pair Programming" for our Platform team to improve code quality. I strongly disagreed. I believed that for our specific work—deep, asynchronous infrastructure research—forced pairing would cut our velocity in half and burn out senior engineers who needed deep focus time. The team was in revolt.

Task: I needed to de-escalate the conflict and move us from "opinion-based arguments" to "data-driven decision making." My goal was to validate the hypothesis (that pairing improves quality) without destroying team morale or velocity in the process.

Action:

1. Strategy (Disagree and Commit): I privately voiced my concerns to my manager but committed to leading the trial. I reframed the mandate into a scientific experiment: "Let's run a 4-week pilot with specific success metrics, and if the data shows it doesn't work, we pivot."
2. Execution (Structured Experimentation): I didn't just say "go pair." I designed three specific pairing models to test:
   • Ping Pong (for TDD/Unit Tests).
   • Strong-Style (for onboarding/knowledge transfer).
   • Async Review (the control group).
3. Leadership: I volunteered to be the first guinea pig, pairing with a junior engineer on a complex ArgoCD pipeline refactor to model the behavior and gather unbiased data.

Result:

• The Pivot: The data showed that "100% pairing" did slow us down, BUT "Strong-Style" pairing reduced onboarding time for new hires by 60%.
• Outcome: We adopted a hybrid model: Pairing is mandatory for onboarding and complex architectural reviews, but optional for routine execution.
• Culture: I turned a toxic "management vs. engineering" conflict into a collaborative process improvement, establishing a culture where we test process changes just like we test code.

5. "What is your greatest weakness?"

Situation: My weakness has historically been "Optimization Bias"—the tendency to seek the mathematically perfect architectural solution at the expense of immediate business velocity. This came to a head when I was architecting the lifecycle management for our Kubernetes platform. The problem space was vast, and I found myself spiraling into designing a complete automated solution that could handle every theoretical edge case, which threatened to delay the MVP.

Task: I recognized that my pursuit of "purity" was becoming a blocker. I needed to shift my mindset from "Architect" to "Product Owner of the Platform" to ensure we delivered value, not just elegant documentation. I had to operationalize pragmatism.

Action:

1. Mechanism (The "Sacrificial Architecture"): I forced myself to define a "Sacrificial Architecture" for Phase 1—components we knew we would throw away. This allowed me to accept imperfection because it was a planned part of the roadmap, not a compromise.
2. Strategic Delegation (The "Red Team"): I actively invited a pragmatic Senior Engineer to challenge my designs. I set up "Red Team" sessions where their specific role was to ask "Do we need this for V1?" This created a feedback loop to counterbalance my bias.
3. Process (Time-Boxed RFCs): I instituted a rule: if a design decision took longer than 4 hours to resolve, it required a "One-Pager RFC" with a decision deadline. This prevented silent rabbit-holing for me and the team.

Result: We delivered the platform 2 weeks early. More importantly, this "Pragmatic Architecture" framework became a team standard. It taught me that the "best" architecture is the one that ships and can be iterated on, not the one that is theoretically perfect.

6. "Walk me through a time you established Operational Readiness."

Situation: At Domain, we had a "Feature Factory" culture. Teams were shipping rapidly, but we faced a "Reliability Cliff"—recurring outages and no clear SLOs because operational rigor was an afterthought. We were accumulating risk faster than we were shipping value.

Task: My goal was to shift the organization from "SRE fixes it" to a "Service Ownership" model. I needed to define a clear "Production Standard" and create a mechanism that made it easier for teams to be compliant than to cut corners.

Action:

1. Strategy (The "Tiered Service Contract"): I defined a "Service Tiering" framework (Tier 0-3). Crucially, I negotiated with Product Leadership that Tier-1 services (Customer Facing) must meet specific non-negotiable criteria (99.9% availability, <12h RTO) to qualify for SRE support. This aligned incentives: "You want our help? You meet the bar."
2. Force Multiplication (The "Readiness Scorecard"): Instead of manually auditing every service, I built an automated "Readiness Scorecard" in our developer portal. It checked for "Must-Haves" (e.g., PagerDuty rotation, Backup policy, structured logs). This gamified the process—teams could see their score and self-remediate without my intervention.
3. Mechanism (Left-Shifting Reliability): I embedded these checks into the SDLC. I introduced a "Pre-Flight Checklist" for Tier-1 launches that included capacity planning and DR testing. This moved reliability from a "post-launch panic" to a "pre-launch gate."

Result:

• Reliability: Uptime improved to 99.98% for Tier-1 services, exceeding our SLA.
• Efficiency: We reduced production incidents by 70% because we caught issues before launch.
• Scale: The framework was adopted by 3 other product teams, becoming the division-wide standard for "Definition of Done."

7. "Walk me through your ArgoCD design for our Kubernetes platform."

Situation: We needed a GitOps foundation capable of scaling to 100+ microservices across multiple clusters. The challenge was managing the entire cluster lifecycle (platform components + apps) and ensuring 20+ teams could deploy safely without stepping on each other. A simple "install" would lead to configuration drift and security risks.

Task: I architected a multi-repo hub and spoke ArgoCD strategy designed for auditability, disaster recovery, and separation of concerns. My goal was to keep it simple while ensuring strict determinism.

Action:

1. Architecture (The "Three-Repo Pattern"): I explicitly separated concerns to manage complexity:
   • Bootstrap Repo (Platform State): I used the "App-of-Apps" pattern to manage platform infrastructure (Cert-Manager, Karpenter, CNI). This allowed us to treat the cluster itself as cattle. We could spin up a new, fully compliant production cluster within 20 minutes.
   • Application Repo (Developer Intent): Developers create standard Helm charts and values that embedding varies subcharts. They define the values they want.
   • Deploy Repo (The "Hydrated State"): This was a critical architectural decision. Instead of letting ArgoCD render Helm charts at runtime (which can be non-deterministic/opaque), I built the CI pipeline to flatten (hydrate) charts into pure manifests and push them here.
2. Why "Hydrated" Manifests?: I championed this pattern to ensure Auditability and Debuggability. If a deployment breaks, we don't debug Helm templates; we look at the exact YAML in the Deploy Repo. It serves as a perfect, immutable snapshot of production state.
3. Security: This design allowed us to implement a "Zero-Touch Production" model. CI pushes to git, ArgoCD pulls from git. Developers never need direct kubectl write access, satisfying our strict compliance requirements.

Result:

• Resilience: We achieved a 20-minute RTO for full cluster disaster recovery.
• Stability: Eliminated "drift" completely. The Deploy Repo is the single source of truth.
• Velocity: Developers deploy with a git commit, yet Operations retains full visibility into the exact artifact versions running via the hydrated repo.

8. Describe a major incident you resolved

Situation: During our critical Saturday auction window, we suffered a cascading failure resulting in 26 minutes of downtime. A 3rd-party API rate-limited us, and our service responded with a "Retry Storm"—aggressively retrying without backoff—which saturated our internal queues and took down the entire auction platform.

Task: As Incident Commander, my immediate goal was to restore service. However, my Principal objective was to address the systemic fragility. We couldn't just "fix the bug"; I needed to architect a defense against cascading failures and shift our culture from "Happy Path" development to "Resiliency First."

Action:

1. Execution (Incident Command): I identified the "Thundering Herd" pattern via ELK logs (90% HTTP 429s). I overrode the team's desire to "hotfix forward" and ordered an immediate rollback to restore stability, prioritizing MTTR (Mean Time To Recovery).
2. Architecture (Systemic Fix): I didn't just patch the loop. I architected a standard Resiliency Layer for all downstream calls. I mandated the implementation of Circuit Breakers (to fail fast) and Exponential Backoff with Jitter (to prevent thundering herds).
3. Force Multiplication (Prevention): I recognized that "checklists" don't prevent code issues. I introduced Chaos Testing into our CI pipeline. We now simulate 3rd-party outages (injecting 429s/500s) during the build. If a service doesn't handle it gracefully, the build fails.

Result:

• Outcome: We have had zero recurrence of retry storms or cascading failures since.
• Reliability: The system now self-heals; when 3rd parties fail, we degrade gracefully instead of crashing.
• Culture: The "Resiliency Standard" I defined is now enforced across all 12 microservices, effectively inoculating the entire platform against this class of failure.

9. Describe a time when you had to balance rapid feature development with system stability

Situation: At Domain, a critical product suite was suffering from "Operational Paralysis." Deployments were manual, infrequent, and risky, causing feature velocity to stagnate. The business demanded a complete modernization (CI/CD, Observability, Branching) within 6 months, but explicitly stated we could not pause feature development to do it.

Task: I needed to change the wheels while driving. My strategy was to decouple "Modernization" from "Feature Work" so they could run in parallel. I aimed to shift the team from a high-friction "GitFlow" model to high-velocity "Trunk-Based Development" without causing a revolt or an outage.

Action:

1. Strategy (The Lighthouse Pattern): I rejected a "Big Bang" rewrite. I selected one low-risk service as a "Lighthouse" project. I personally migrated this service to ECS, enforcing backwards compatibility with zero downtime deployment and Trunk-Based Development. This served two purposes: it validated the architecture and provided a tangible "win" to win over skeptical stakeholders.
2. Force Multiplication (The Migration Kit): I didn't want to be the bottleneck for the other 5 services. I packaged my work on the Lighthouse service into a "Self-Service Migration Kit", including standardized Terraform modules, CI templates, and a step-by-step runbook.
3. Execution (Federated Migration): I empowered the product developers to migrate the remaining 5 services themselves using my kit. I shifted my role to "Consultant," focusing on adding deep Observability (Kibana dashboards, structured logging) and running Incident Management workshops to upskill the team on the new stack.

Result:

• Velocity: We completed the migration in 5 months (1 month early). Deployment frequency increased from "bi-weekly" to "multiple times per week".
• Autonomy: The product team now fully owns their stack. They migrated 5 services with minimal intervention from me.
• Stability: We achieved this with zero downtime during the transition, proving that stability and speed can coexist.

10. Tell me about a time you disagreed with product engineers on technical direction

Situation: While seconded to a product team, I identified a critical risk during sprint planning. The team was pushing to release two complex features, but our Error Budget for the 30-day SLA window was effectively exhausted (<5 minutes remaining). We were flying without a safety net, and the team was prioritizing "Output" (features) over "Outcome" (Reliability/SLA compliance).

Task: I needed to shift the decision-making framework from "Opinion-Based" ("we need these features") to "Data-Driven" ("we cannot afford the risk"). My goal was to enforce the Error Budget not just as a metric, but as a governance mechanism to protect the business from SLA breach penalties.

Action:

1. Strategy (Data Visualization): I started with data and presented a projection showing that even a minor regression from the new features would breach our 99.9% SLA within 4 hours, triggering contractual penalties. I reframed the decision: "We aren't choosing between features and bugs; we are choosing between features and a contract violation."
2. Leadership (The "Reliability Sprint"): I proposed a "Reliability Sprint" compromise. We would freeze feature work for 2 weeks to pay down the specific technical debt causing the recurring bugs. I aligned stakeholders by explaining that this investment would replenish our error budget, allowing faster feature velocity in the next Quarter.
3. Mechanism (Policy as Code): To prevent this debate from recurring, I suggested an "Code Yellow" policy. If the Error Budget drops below 10%, our CI/CD pipeline automatically blocks non-critical feature deployments. This turned a subjective argument into an objective system rule.

Result:

• Outcome: The team agreed to the freeze. We fixed the top 3 recurring bugs, restoring the Error Budget to a healthy 99.95%.
• Business Impact: We avoided the SLA breach and the associated financial penalties.
• Culture: The "Error Budget" is now a primary KPI in sprint planning, shifting the culture from "Feature Factory" to "Sustainable Engineering."