I build end-to-end data pipelines on AWS — ingestion, cleaning, transformation, orchestration, and persistence — with Python and PostgreSQL. 10+ years across ETL, databases, and backend systems, with a practical focus on reliability, performance, and cost.
A production data pipeline unifying four partner feeds — three barcode encodings, four delivery formats — into one PostgreSQL system on AWS. ~11.4M records/year.
read case study →A tool to help runners craft terrain specific pacing plans.
Design and deploy end-to-end data pipelines for clients — ingesting from flat files and REST APIs, then cleaning, merging, transforming, and orchestrating in Python. Build on AWS using RDS (PostgreSQL) for persistence, Lambda for compute, S3 for storage, and CloudFront for delivery, with an emphasis on reliability, cost, and clean operational handoff. Own data quality, error handling, retry logic, and monitoring across production workloads, and produce runbooks so client teams can operate pipelines without ongoing dependence on the engineer.
Senior escalation engineer for a data discovery platform processing billions of records across enterprise Microsoft 365, SQL Server, PostgreSQL, and NoSQL sources. Diagnosed and resolved issues in backend services, data pipelines, authentication layers, and discovery agents across Windows, macOS, and Linux. Partnered with Engineering and QA on production-defect root cause analysis, influencing architectural changes that improved reliability and data-pipeline performance. Owned documentation and release readiness, expanding the knowledge base by 100%, and mentored two engineers on data-focused troubleshooting and reproduction methodology.
Designed and automated enterprise-scale data discovery workflows across Microsoft 365, SQL, and NoSQL systems, supporting 30+ projects ($50K–$300K ARR). Built custom scripts and integration layers in Python, PowerShell, and REST APIs to ingest, transform, and classify enterprise data. Tuned backend configurations and database indexing to cut scan execution time by 50%, and worked directly with customer data and engineering teams on data architecture, API usage, and scaling strategies.
Engineered ETL pipelines using SSIS and T-SQL to support enterprise reporting, analytics, and automation across 20+ projects (budgets up to $500K each). Designed backend modules and batch jobs to ingest, transform, and persist high-volume data at scale. Optimized SQL Server performance through indexing, query refactoring, and workload tuning, improving query execution time by 50%. Led architecture and delivery of end-to-end automation solutions across finance and operations for 10+ cross-functional departments.
Led a team of 5 engineers resolving 200+ production cases per month, many involving ETL jobs, database issues, and data-integrity problems. Acted as technical liaison between Engineering, QA, and Customer Success to triage data and platform issues.
Diagnosed and resolved complex hardware and software issues with a focus on quality, privacy, and data recovery. Led in-store technical triage for up to 40+ repairs per day.