I build reliable ETL and streaming systems, optimize pipeline performance, and deliver analytics-ready datasets. Strong focus on orchestration, data quality, and production troubleshooting.
Production-style projects with clear architecture, tradeoffs, and measurable outcomes.
End-to-end ETL with orchestration, validation, Spark transformations, and warehouse loading. Includes performance tuning and failure recovery patterns.
Streaming pipeline for transaction events with event-time handling, deduplication, and checkpoint-based recovery.
Unified customer model built from multiple systems with incremental processing and quality gates.
Framework to detect “pipeline succeeded but data is wrong” using anomaly signals and freshness metrics.
AI-augmented pipeline that detects anomalies in transaction data and delivers plain-English root cause analysis to Slack — automatically, without manual log triage.
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Focused skill set aligned to modern data engineering roles.
Python, SQL, Spark/PySpark, Airflow, Kafka, ML workflows, Data Modeling, Claude API, AI, ETL/ELT, Data Quality
AWS (S3, Athena, EC2), Warehousing (Redshift), Azure Data Factory, Azure Synapse, Azure Data Lake, CI/CD, Docker, Monitoring & Alerting
Impact-focused work with production ownership.