Worked on Chalk AI’s documentation and backend systems, focusing on data engineering and observability. Built and documented the continuous backfills concept for timeseries aggregations in the chalk-ai/docs repository, enabling users to configure scheduled backfills and continuous buffer durations for fresher aggregate computations. In chalk-ai/chalk-go, expanded observability by implementing resolver invoker metrics instrumentation using Protocol Buffers and Go, introducing a dedicated metric namespace to support tracking and analysis of resolver performance. Prioritized clear documentation and maintainable code, aligning protobuf definitions with metrics strategies to facilitate future analytics and dashboards. Work emphasized robust documentation, metrics, and protocol-driven engineering practices.