I spent ~14 months on an energy-market automation platform for hydropower operators, focused on generating and submitting daily electricity market bids.

At its core was a bid-processing pipeline: when new price scenarios arrived, the system triggered an orchestrated chain of serverless functions to (1) fan out parallel solver runs across scenario permutations, (2) collect partial bid outputs per asset, and (3) aggregate them into a final bid matrix ready for submission. This ran on a daily cadence at scale and was designed to be fully automated and operationally robust.

The pipeline used a workflow/orchestration engine with dynamic parallelism. A dispatcher step evaluated configuration at runtime and produced variable-length task lists, which the workflow engine expanded into parallel function executions. This allowed parallelism to scale with configuration rather than requiring code changes. Multiple workflow definitions covered the end-to-end lifecycle, including backtesting/benchmarking bid methods against historical data.

The implementation consisted of a suite of idempotent Python serverless functions with explicit status tracking and structured event metadata. Error handling favored graceful degradation: if all solver runs failed, the system raised alerts and preserved context for troubleshooting; if only some runs failed, it marked partial outputs and continued aggregation when safe. Execution timeouts were stratified by stage to match expected runtimes (short dispatch, longer simulation, moderate aggregation).

My largest contribution was leading a migration from legacy data models to a flexible domain modeling approach. This included refactoring data access, redefining function inputs/outputs, and removing legacy transformation paths. I also built and improved integration testing infrastructure (mocking external approvals, fixture-driven test cases), consolidated components into a monorepo, and refactored key sections of the bid-matrix pipeline for maintainability.

Beyond the core pipeline, I contributed to shared developer tooling and libraries used across the platform, including SDK improvements, code generation utilities, solver-adjacent wrappers, and domain schema work.

 

 

Client name and identifying details withheld due to confidentiality. Screens and data are recreated/illustrative; no proprietary assets included.