Data Engineer

Strobe is building the world's largest power plant: not a single site, but a distributed fleet of buildings, batteries, EVs, and generators that buy and sell power in real time across wholesale energy markets. Own the data that runs the fleet: telemetry from every site, utility bills and interval data, and wholesale market prices, cleaned, joined, and ready for dispatch, forecasting, billing, and settlement.

What you'd own Pipelines that ingest utility interval, billing, and meter data through Green Button Connect (ConEd, PG&E, SCE) and other utility sources Wholesale market data from NYISO, PJM, and CAISO: real-time and day-ahead prices, load, capacity, and settlement files Site telemetry at fleet scale: time series from batteries, generators, and meters The data models that dispatch, forecasting, bill validation, and customer reporting all read from Data quality: freshness and gap alarms, backfills, and reconciliation against utility bills and ISO settlements Weather and load inputs for forecasting Strong fit if you have Strong Python and SQL, and production pipelines you built and ran end to end Time-series and columnar data at scale (ClickHouse or similar) AWS data tooling (Lambda, S3, Kinesis, EventBridge) managed through Terraform Experience with messy external data: inconsistent APIs, late or revised data, timezone and DST edge cases Rigor about correctness when the numbers end up on a customer's bill or in a market settlement Bonus points Familiarity with utility tariffs, interval data (ESPI / Green Button), or ISO market data Forecasting or ML feature pipelines Dashboards and data observability (Grafana or similar) Stack: Python / SQL / AWS (Lambda, S3, Kinesis, EventBridge) / ClickHouse / Terraform / Grafana.

AI-agent-native monorepo with deep investment in agent-enabled engineering efficiency. Small team, high ownership, no layers.