Data Engineer
From raw ingestion to production-ready pipelines — systems that teams can trust, monitor, and build on top of.
Design and build pipelines that process, transform, and reconcile large datasets reliably across systems.
Automated checks, validation rules, and monitoring that prevent regressions and ensure data reliability.
Well-defined metrics and structured datasets that make analysis faster and more trustworthy.
01 / 06
Case study · Financial Services
Reconciled 50+ datasets with higher accuracy and faster turnaround using a scalable matching system.
02 / 06
Case study · ML Infrastructure
Stabilized ML-based fraud detection by building automated data quality guardrails before model ingestion.
03 / 06
Case study · Financial Reporting
Built a governance and profiling foundation that made financial reporting trustworthy, explainable, and auditable.
04 / 06
Case study · Data Quality
Designed validation workflows to confirm fixes were correct and regressions were prevented post-deployment.
05 / 06
Project · ML / Analytics
Evaluated polynomial regression models across complexity levels using cross-validation to analyze bias-variance tradeoffs.
06 / 06
Project · ML / Analytics
Built a transparent OLS baseline with multi-metric evaluation to establish trust before introducing model complexity.
The technical domains I work in daily — and the tools I reach for when solving hard problems at scale.
Start with the reconciliation pipeline →Programming
BI Tools
Cloud
Databases
Domain Expertise