Alexis Abbott

About

Backend/data engineer with strong AWS production experience spanning serverless APIs and containerized workers (Lambda, API Gateway, ECS) plus data pipelines (Glue, S3, Athena/Redshift). Has modernized legacy SAS/cron batch systems into Python/AWS with parallel-run parity validation and low-risk cutovers, and has owned ETL incidents end-to-end (CloudWatch detection, backfills, and preventative controls). Targeting $130k–$150k base and strongly prefers remote, with occasional Bethesda onsite acceptable.

Experience

Education

Key Strengths

• Designed and operated AWS notification + data-ingestion backend using API Gateway, Lambda, DynamoDB, SNS, and S3
• Reliability-focused API design: JWT validation, explicit timeouts, idempotent retries with exponential backoff/jitter, structured errors, correlation IDs
• Production deployments with IaC (CloudFormation/Terraform) and CI/CD including automated tests, versioned artifacts, and safe rollbacks
• Cost/performance optimization across Lambda and ECS (right-sizing, reserved concurrency, shifting sustained workloads to ECS)
• Built AWS Glue ETL to curated partitioned Parquet in S3 for Athena/Redshift with schema evolution and data-quality quarantine
• Modernized legacy SAS/cron batch logic to Python + AWS jobs using parallel runs, stakeholder-facing parity reporting, phased rollout, feature flags, and rollback
• SQL performance tuning with measurable impact (reduced reporting query runtime from minutes to seconds via composite index, early filtering, partition batching)
• Incident ownership for data pipelines: detected via CloudWatch, triaged with logs/metrics, paused downstream consumers, backfilled partitions, added preventative validation/runbooks