Data Engineering at Scale
Retired an entire failure class with a structural fix instead of a workaround.
Moved raw-volume-sized staging out of the database (PostgreSQL) and into an on-task columnar pre-aggregation step (DuckDB). Now database staging scales with deduplicated volume instead of raw volume, closing a disk-exhaustion failure that had previously killed a production load outright. (Payor Rate Data Platform, the rate-extraction pipeline's DuckDB pre-aggregation stage)
Demonstrated dedupe and collapse ratios at production scale.
One payer's raw feed collapsed roughly 9-20x through pre-aggregation. A single payer's monthly raw volume of about 200 million rows reduced to a multi-million-row deduplicated serving grain. (Payor Rate Data Platform, rate-extraction pipeline pre-aggregation metrics and the July 2026 serving-state report)
Designed ingestion to survive task termination instead of restarting from zero.
For multi-hour fetches, each source file uploads to S3 as soon as it completes, not at the end of the run. On restart, the job lists what's already banked and fetches only the gap. That turns a killed multi-day fetch into a cheap top-up instead of a full re-run. (Payor Rate Data Platform, ingestion pipeline's per-file upload and resume design)
Built automated, per-source verification that catches a missing feed on its own.
A read-only monthly job, written in Python, checks that every expected data source landed above a floor. The floors are documented plainly as rough placeholder estimates, to be recalibrated once real levels are known, not presented as already tuned. (Payor Rate Data Platform, the pipeline's serving-completeness verification job)
Built a living dataset catalog that ties schema to business questions.
Cross-references every measurable field against multiple analytical lenses and maps each one to a persona-grounded SQL query library. This gives a self-service trace from raw schema to a product-facing question. (Payor Rate Data Platform, the pipeline's dataset catalog and query-library documentation)
Derived data-quality heuristics from real per-payer data instead of one global rule.
Built dead-man checks in Python for a structural anomaly class (plan-grain collapse or explosion), with per-payer exceptions. Each exception is justified by an actual measured ratio against that payer's real monthly data, not a single global threshold. (Payor Rate Data Platform, the pipeline design's observability and dead-man-check specification)
Kept a data-quality signal advisory rather than using it to silently drop rows.
Computed a suspicious-provider-share metric in Python. Code comments and schema documentation state that it exists to inform downstream judgment, not to filter automatically, preventing silent data loss from a heuristic. (Payor Rate Data Platform, rate-extraction pipeline's serving-state documentation)