• Led the development of the Delta Flink connector, allowing Apache Flink to write Delta Lake tables. Engineering point of contact for multi-million-dollar customer. Investigated and shipped critical correctness (data loss) and performance improvements, reducing initialization time by 45x and CPU usage by 8x. Designed and productionized cross-engine, multi-cluster concurrent writes from Flink and Databricks Runtime into S3.

• Helped develop Delta Kernel, a new abstraction over the Delta protocol that allows engines to build simpler Delta connectors using narrow APIs. Specifically, focused on Delta log metadata replay and performance. Designed and shipped

new 'hint' algorithm to improve initial snapshot schema loading by 34x. Investigated CPU bottlenecks to further improve performance 3x.

• Helped develop Delta Universal Format, a Delta feature that allows converting Delta metadata to Apache Iceberg. Designed and implemented the Iceberg Compatibility V1 table feature, which protects Delta tables from operations that would make them incompatible with Iceberg.

• Unblocked Model Serving Inference Tables on AWS by designing a single-node client to coordinate Delta metadata commits to S3 via the Databricks S3 commit service.

• Designed from the ground up internal tooling, auditing, and test systems for all Delta development at Databricks. Decreased test run time 30x from 3+ hours to ~6 minutes.

• Developed the Delta Standalone Writer, a Spark-less connector to Delta Lake that writes transaction log metadata while maintaining ACID guarantees.

• Designed and developed S3 multi-cluster write functionality for Delta Lake, implementing a new cloud store API that uses DynamoDB to provide the mutual exclusion that S3 is lacking.

• Developed new performance and functionality features for Delta Lake including data-skipping using per-column statistics and Change Data Feed .

Designed, built, and released a Spark-less transaction log and data reader Java SDK (written in Scala) to make it easier for other query engines to connect to Delta Lake.

Also developed a new end-to-end benchmarking framework to test the performance of Delta Lake using simulated real-world workloads.

• Improved build stability and failure observability by migrating key Jenkins jobs to internal build system Runbot. Included simpler configuration, automated metrics, Spinnaker pipeline integrations [Python, Jsonnet]

• Let devs easily find origin of flaky test failures through new search, filter Runbot API endpoints [Scala, Quill]

• Increased runtime exception triaging accuracy to teams using owner-wrapping framework [Scala, Sentry]

• Designed Runbot dashboard views, automated job-tagging infrastructure for concise per-team UIs [ScalaJS]