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Fuse Energy is an AI-first energy company building a fully integrated energy system combining solar, batteries, grid infrastructure, real-time power trading, and distributed energy solutions for consumers. The company has raised over $200M from top-tier investors including Balderton, Lakestar, Accel, and Creandum.
You will own trading data end-to-end as a product, from external data ingestion through to delivery to traders and quantitative models. You are accountable for data correctness, timeliness, reproducibility, and utility. Every model and trading decision depends on your work, making this role critical to the desk's capabilities.
Key responsibilities include:
- Own trading data as a product covering power market data, weather forecasts, grid and system data, fuel prices, and fundamentals, ensuring reliability and usefulness to traders and quants
- Ship datasets and pipelines to production quickly and identify and fix performance bottlenecks across ingestion, storage, and delivery
- Design canonical schemas supporting point-in-time correctness, revisions, and backtesting without lookahead bias
- Evaluate, onboard, and manage data vendors, balancing coverage, latency, licensing, and cost against trading value
- Build and run monitoring for freshness, completeness, and anomalies with clear incident response processes
- Lead and grow a team of engineers, mapping data flows across the business to enable rapid source integration and prevent downstream issues
Requirements:
- Proven track record owning and shipping data products to real users or customers, including pipelines, data quality, delivery, and accountability for outcomes
- Experience leading people and coordinating across teams with clear understanding of data flow through a business
- Technical rigor: ability to profile systems deeply and untangle performance bottlenecks
- Strong schema design for time series and event data, including revisions, late-arriving data, and entity mapping across vendors
- Strong Python and SQL with production pipeline experience at scale
- Experience evaluating and negotiating with data vendors with cost-versus-value perspective
Nice to have:
- Built or ran a data vendor business or owned comparable data product end-to-end
- Background in trading firm, hedge fund, or other market-facing environment
