Platform ownership: proposed and own a declarative data-contract system that is the platform's core data-processing stage — it replaced the entire legacy U-SQL processing layer in the migration to Spark on Synapse — and teams use it to produce canonical, governed datasets (schemas, validation, governance, delivery SLAs) self-served across Partner, Seller, and Royalties orgs without modifying platform code.
Data-integrity quality gates: replaced manual contract-review checks with automated validation built into the pipeline, removing humans from the critical path while raising the quality bar across Partner, Seller, and Royalties orgs.
Reporting & metrics: build the ADX (Kusto) dashboards that track SLA, performance, and COGS — the primary way platform delivery health and cost are reported.
Unified cross-team reporting: built Spark observers that aggregate cross-team metrics into a unified reporting layer, and lead cross-team coordination on the next-generation platform for the incentives space.
Resilient orchestration: Azure Data Factory and Synapse pipelines, plus custom Azure Functions orchestration for cross-region redundancy — pipelines fail over to a backup region when the primary is down and resume on restore.
Developer experience: drive faster local Spark workflows, self-service onboarding for new entities and teams, and reliability tooling (SLA / quarantine / alerting) so daily work is predictable.
Production at scale: the platform processes 2+ TB/month representing $110B in revenue; the Spark/Synapse rebuild delivered ~8× E2E speedup at 1/5 the cost.
Also helped move an existing big-data platform from the commercial cloud to a secure federal environment.