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Shawn Williams

Mid-Level Backend Engineer specializing in AWS serverless and data processing

Software Development Engineer II7 years experienceMid-LevelMedia & EntertainmentStreaming MediaE-commerce
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About

Amazon Prime Video backend engineer who built and operated high-traffic Python/FastAPI services and AWS-native data/batch systems. Demonstrates strong production reliability and incident ownership (CloudWatch/X-Ray), plus measurable performance wins (8s to <200ms query latency, ~40% CPU reduction) and cost-focused architectures (Lambda + ECS/Fargate with Fargate Spot).

Experience

Software Development Engineer IIAmazon
Software EngineerAmazon
Associate Software EngineerCornerstone OnDemand
Software Engineering InternshipsSAP Ariba
Software Engineering InternshipsLendUp
Software Engineering InternshipsMastercard
Software Engineering InternshipsCarrington Mortgage Holdings

Education

University of California, Irvinebachelor, Computer Engineering (2018)

Key Strengths

  • Built high-scale Python/FastAPI services handling thousands of concurrent I/O requests
  • Strong security/auth design using AWS SigV4 with request interception and IAM principal validation
  • Reliability engineering: retries/timeouts with Tenacity (exponential backoff + jitter) and fail-fast error handling
  • Delivered hybrid serverless + containers architecture (Lambda orchestration + ECS/Fargate for heavy processing) with IaC and blue-green CI/CD
  • Cost optimization using Fargate Spot (reported up to ~70% savings) and autoscaling via ECS target tracking
  • Designed AWS Glue ETL from S3 telemetry to partitioned Parquet with schema evolution via Crawlers/Data Catalog and data quality checks
  • Built resilient batch recovery using Step Functions + Lambda log inspection and Glue Bookmarks to avoid reprocessing
  • Modernized legacy monolith/stored-procedure system into Python microservices with canary rollout (5% increments) and parity reporting
  • SQL performance optimization with measured impact: reduced query latency from ~8s to <200ms and cut CPU ~40%
  • Incident ownership: detected batch stall via CloudWatch alarm, triaged with Logs/X-Ray, mitigated via caching and catch-up job, prevented recurrence by decoupling with SQS + Lambda

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Contact

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Languages

English

Skills

PythonSQLT-SQLBashPowerShellJavaScriptTypeScriptRGoC#RESTful APIsGraphQLHTMLCSSDocker