Staff-Track Backend Engineer

- Partner transactions were arriving out-of-order and causing duplicate processing; designed idempotent eventdriven pipelines on AWS Lambda + SQS/SNS with exactly-once guards and adaptive back-pressure, handling 7,200+ payer connections without data loss. - Integration drift between internal teams was causing weekly regressions; led OpenAPI-first contract design for X12/EDI endpoints (837P/I/D, 270/271), wired contract tests into GitHub Actions CI, and automated SDK generation — cutting integration-related incidents by ~70%. - Eligibility reads were timing out under query spikes; designed single-table DynamoDB access patterns alongside CQRS Postgres read models, bringing p95 read latency to under 10 ms while keeping analytics workloads fully decoupled. - External payer outages were triggering cascading failures and unnecessary pages; added circuit breakers, exponential-backoff retries, and dead-letter queues to all EDI/API call paths, reducing pager-fatigue incidents by ~60%. - Service boundaries lacked consistent auth controls, creating HIPAA exposure risk; implemented OAuth2 + JWT with fine-grained RBAC and automated KMS secrets rotation across all services, passing SOC 2 Type II audit with zero findings. - On-call engineers had no actionable signal during degradations; standardized structured CloudWatch logging, distributed traces, and Prometheus-style metrics with SLO dashboards covering p95/p99 latency and error budgets — reducing mean time to detect (MTTD) by ~45%. - Critical claim-submission flows had no tested failure playbooks; led threat-modeling and chaos drills (fault injection, payer-outage simulation), producing runbooks that cut mean time to resolve (MTTR) from ~40 min to under 15 min. - Enterprise customers struggled to onboard complex X12 EDI requirements; collaborated with network-ops and implementation-engineering teams to translate EDI schemas into developer-friendly JSON API contracts, reducing customer integration timelines by ~30%.

- Real-time promotion evaluation was breaching SLAs during peak traffic (Black Friday, brand launches); profiled and re-engineered Go hot paths — goroutine pools, connection reuse, rule short-circuiting — reducing p99 evaluation latency by ~55% under 3× normal load. - Campaign authoring writes were contending with read-heavy evaluation queries and causing timeouts; proposed and implemented CQRS with a DDD-modeled rules engine (custom Go lisp dialect), decoupling write and read paths and eliminating evaluation timeouts during authoring peaks. - Coupon double-spend and reward duplication were eroding client trust; designed and shipped transactional outbox pattern with message-level deduplication for reward issuance, achieving exactly-once semantics and reducing fraud incidents to near zero. - High-risk rollouts to enterprise clients (Adidas, Sephora, Carlsberg) were too risky to ship all at once; built canary and shadow deployment pipelines in CI/CD, enabling staged validation and reducing production incidents from new releases by ~65%. - Promotion lookups were causing database saturation during flash-sale peaks; tuned Postgres with composite indexes and partitioning, layered Redis caching for hot campaign reads — sustaining 5× peak load without vertical scaling. - Alert fatigue was degrading on-call quality; established SLOs and error budgets, wired Prometheus alerts to scoped runbooks — cutting actionable-to-noise alert ratio by ~3× and reducing mean time to acknowledge (MTTA) by ~40%. - Mid-level engineers were producing inconsistent API designs and under-tested services; ran regular Go code reviews, load-testing workshops, and architecture design sessions across squads, measurably improving p99 latency baselines and test coverage on reviewed services. - Three major API versions needed to ship without breaking existing enterprise integrations; drove versioned, backward-compatible OpenAPI contract strategy across the promotion engine, enabling zero-breaking-change upgrades and unblocking client adoption in EMEA and APAC.

- A tightly coupled .NET monolith was blocking independent deployments across reservations, invoicing, and payments; owned and drove the strangler-fig decomposition with Clean Architecture boundaries on .NET 5→6, enabling teams to ship independently and eliminating cross-domain deploy conflicts. - Payment and reservation workflows were losing events during downstream failures; redesigned them as async event-driven flows on Azure Service Bus topics, improving fault isolation and reducing lost-event incidents to zero over the final 6 months. - Schema drift with Mews Marketplace's 500+ partner integrations was causing weekly regression incidents; introduced OpenAPI-driven contracts with automated schema validation in CI, cutting partner integration regressions by ~80%. - High-read reservation endpoints were overloading Azure SQL under peak hotel check-in periods; added composite indexes, query-plan tuning, and Redis-backed response caching, contributing to a 2× reduction in p95 API response time achieved during the .NET 6 migration. - Deployments were causing brief downtime that violated hotel-operations SLAs; implemented zero-downtime blue/green slot swaps on Azure App Service with readiness probes and graceful shutdown in Kubernetes — achieving 100% zero-downtime deploys post-rollout. - Per-tenant authorization was inconsistent across services, creating cross-tenant data exposure risk; unified auth with OAuth2 scope-based access control and per-tenant RBAC, and centralized secrets in Azure Key Vault, remediating all critical findings from an internal security review. - Core guest journey reliability was invisible until customers reported issues; instrumented end-to-end distributed traces and error-budget dashboards in New Relic for booking, check-in, and payment flows — reducing customerreported reliability incidents by ~50%. - Analytics refresh was running on multi-hour batch cycles, making operational reports stale; designed and delivered Azure Databricks ingestion pipelines into the shared data warehouse, cutting analytics update latency from hours to under 15 minutes.

- GraphQL and REST endpoints for user-generated content were timing out under angler-activity spikes; optimized Rails 6 query layers, added efficient cursor pagination and Pundit-scoped authorization caching, stabilizing p99 latency for 14 M+ user interactions. - Catch-data aggregation jobs were blocking the Rails process pool and degrading API response times; migrated ingestion pipelines to concurrent Go microservices, improving throughput ~3× and eliminating p99 degradation during aggregation runs. - Heavy analytics computations were spiking DB CPU during peak hours; designed Postgres composite indexes and offloaded computation to Sidekiq background jobs, keeping real-time API p99 latency flat during data-processing windows. - Primary DB was receiving all reads and writes equally, causing avoidable load spikes; implemented read-replica routing for reporting endpoints as part of the team's Aurora MySQL migration, reducing primary DB load by ~40% at peak without custom connection-routing code. - New features were rolling out to all 14 M users simultaneously with no rollback path; added feature-flag hooks and A/B test instrumentation to several endpoints, enabling the team to run controlled rollouts and make data-driven activation decisions. - Media-serving origin was absorbing high bandwidth cost from angler photo and video uploads; implemented S3 lifecycle policies and CloudFront CDN caching for the media pipeline, reducing origin load by ~55% and cutting egress spend materially. - The platform team had no proactive alerting on API errors or DB lag, reacting only to user reports; added structured logging and error-rate alerts on key endpoints and DB replica lag metrics — shifting incident detection from userreported to automated. - Product had limited visibility into where users dropped off in key funnels; instrumented conversion events and retention signals on several backend flows, enabling product experiments that improved feature activation in subsequent A/B tests.

- The V8 time-entry API was a bottleneck for billing and reporting services due to synchronous coupling; built Go backend services with gRPC for low-latency inter-service communication, reducing reporting query latency by ~40% and enabling independent scaling. - Analytics and billing reconciliation depended on stale batch exports, causing delayed reporting; implemented PostgreSQL logical replication and CDC pipelines for near-real-time data feeds, cutting analytics staleness from hours to under 2 minutes. - Frequent, risky deployments to a monolithic GCE setup were limiting release confidence; contributed to the team's GKE migration by containerizing owned services and configuring HPA and PodDisruptionBudgets, helping increase deploy frequency and eliminate service-level downtime during releases. - Inconsistent error models and missing request validation across Go services were causing integration bugs; authored OpenAPI specs with middleware-level request validation and uniform error response structures for owned services, improving API contract consistency. - Infrastructure for owned GCP services was manually provisioned and not reproducible; wrote Terraform modules for GKE workloads, Cloud SQL instances, and Cloud Storage buckets, reducing manual setup time and enabling consistent environment creation. - Hot paths in the high-frequency time-entry write flow were causing latency spikes at scale; used RED/USE metrics and distributed tracing to identify bottlenecks, then optimized the critical write path — reducing average response time by ~35% on targeted endpoints. - Flaky tests were causing false-negative CI builds and slowing review cycles; identified root causes of the most frequent failures in owned test suites and fixed ~60% of them, contributing to a broader cross-team effort that cut the overall flaky-test rate from ~15% to under 2%. - Reporting dashboards showed stale data minutes after backend updates; built React UI components consuming updated WebSocket-backed API endpoints, reducing the visible data-freshness gap from minutes to seconds for active workspace users.

- Content publishing was failing silently when webhook payloads were malformed, confusing early customers; implemented TypeScript/Node.js validation middleware and error-response standardization for the webhook dispatch flow, reducing unhandled silent failures on assigned publishing endpoints. - Several GraphQL query fields lacked input validation, causing unpredictable backend errors; added schema-level validation and sanitization for assigned query types under senior guidance, improving error consistency on the content delivery API. - Content retrieval queries on large workspaces were noticeably slow; identified missing indexes on frequently filtered columns, wrote and tested Postgres migrations, and verified query-plan improvements — reducing p95 response time on the targeted retrieval endpoints. - The content API had no automated regression safety net for schema changes; wrote Jest unit and contract tests for the features I owned and integrated them into the CI pipeline, catching several schema regressions before they reached staging. - Local development environments differed from CI, causing 'works on my machine' failures; dockerized owned Node.js services using multi-stage images with consistent base versions and added readiness probes, aligning local and CI build behavior. - Code style was inconsistently enforced across the Node.js codebase, causing noisy PR reviews; set up ESLint rules and Husky pre-commit hooks for the services I worked on, reducing style-related review comments on my pull requests. - Handoff of completed features was slow because context was only in Slack threads; wrote concise documentation for each feature I shipped, covering API behavior, edge cases, and rationale — making it easier for the senior engineer reviewing my work to validate and merge faster.