Data Engineering
Pipelines, warehouses, streaming, and data quality engineering that analysts can trust.
Playbook: Warehouse from zero to trusted
Contracted schemas, tested tables, dimensional models analysts rely on.
Batch Pipelines
Idempotent stages, partitioned writes, and backfill strategies so failures heal instead of corrupt.
Schema contracts, compatibility modes, and deprecation flows across pipeline boundaries.
Layered data quality tests that block bad data from reaching downstream consumers.
Layered data quality tests that block bad data from reaching downstream consumers.
Layered data quality tests that block bad data from reaching downstream consumers.
Streaming & Modeling
Event-time vs processing-time decisions, late-data handling, and exactly-once semantics.
Facts, dimensions, SCD strategy, and grain declarations serving analytics for years.
Lakehouse & Streaming
Lambda-to-Kappa migrations, microbatch cadences, and consistency contracts across paths.
Iceberg/Delta/Hudi tradeoffs, compaction strategy, and catalog governance preventing metadata debt.
Journey Playbooks
Contracted schemas, tested tables, dimensional models analysts rely on.
Rerunnable stages, freshness SLAs, and alert fatigue eliminated.
Correct event-time semantics with bounded state and monitored watermarks.
Stream/batch boundaries unified with open table formats governed properly.