Data Platform Lead
富士是一个人工智能第一能源公司,其使命是使能源充足和经济实惠,快速。我们将先进的理念与最尖端技术结合起来来建立更好的能源系统。我们从包括巴尔德顿,莱克斯塔,埃塞尔,克里兰都姆,低碳,利比特,20维奇,哈密伯德和协作基金等顶级投资者那里筹集了超过200万美元,以及 Nico Rosberg 和 GPs 在后面的战略天使,如 Revolution,Spotify 和 Uber.我们正在建设一个完全一体化的能源公司:通过直接向消费者销售,我们削减中间人,降低成本并将储蓄转移到我们的客户。我们正在聘请数据领先者以拥有交易数据端末,从外部数据进入 Fuse 的时刻到一个商家或模型依赖它的那一刻。你将负责这些数据是否正确,及时,可复制和实际有用,并且你将它们快速投入生产。我们所做出的每一个模式和交易决定都取决于它,因此这个角色设定了桌面能够做的限量。责任 · 作为产品拥有贸易数据,涵盖电力市场数据,天气预测,电网和系统数据,燃料价格和基础知识,并负责其可靠性和对交易者和量子者的有用性。· 将数据集和管道快速输入生产,在摄入,存储和交付过程中找到和解决性能瓶。·设计规范式方案,支持时刻正确度,修改和后检,而不需要偏见,应用严谨的效果,并在不需要的地方迅速移动。· 评估,集成和管理数据供应商,包括覆盖率,延迟时间,许可和成本与交易价值相比。· 在输入中断时建立并运行清晰的过程来监测新鲜度,完整性和异常情况。· 领导和发展工程师团队并绘制整个业务中的数据流动方式,以便你能够快速插入新的来源并阻止导致下游问题的决策。要求 · 对拥有和向实际用户或客户发送数据产品的记录进行追踪,包括管道,数据质量和交付,并且负责结果而不是仅实施。· 领导人员的记录和团队间协调,清楚了解如何进行工作。
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Fuse is an AI-first energy company on a mission to make energy abundant and affordable, fast. We combine first-principles thinking with cutting-edge technology to build a radically better energy system.
We've raised over $200M from top-tier investors including Balderton, Lakestar, Accel, Creandum, Lowercarbon, Ribbit, 20VC, Hummingbird and Collaborative Fund, alongside strategic angels including Nico Rosberg and GPs behind Meta, Revolut, Spotify and Uber.
We're building a fully integrated energy company: developing our own solar, batteries and other generation projects, building our own hardware, improving and developing grid infrastructure, trading power in real time, and installing distributed energy in homes. By selling directly to consumers, we cut out the middleman, lower costs and pass the savings on to our customers.
We’re hiring a Data Lead to own trading data end to end, from the moment external data enters Fuse to the moment a trader or model relies on it. You’ll be accountable for whether that data is correct, timely, reproducible and actually useful, and you’ll get it into production fast. Every model and trading decision we make depends on it, so this role sets the ceiling on what the desk can do.
Responsibilities
- Own trading data as a product, covering power market data, weather forecasts, grid and system data, fuel prices and fundamentals, and be accountable for its reliability and usefulness to traders and quants.
- Ship datasets and pipelines to production quickly and find and fix performance bottlenecks across ingestion, storage and delivery.
- Design canonical schemas that support point-in-time correctness, revisions and backtesting without lookahead bias, applying rigour where it pays off and moving fast where it doesn’t.
- Evaluate, onboard and manage data vendors, including coverage, latency, licensing and cost against trading value.
- Build and run monitoring for freshness, completeness and anomalies with a clear process when a feed breaks.
- Lead and grow a team of engineers and map how data flows across the business so you can plug in new sources quickly and block decisions that would cause problems downstream.
Requirements
- Track record of owning and shipping data products to real users or customers, including the pipelines, data quality and delivery, and being accountable for outcomes rather than only the implementation.
- Track record of leading people and coordinating across teams with a clear understanding of how data flows through a business.
- Technical rigour: you can go deep, profile a system 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 and experience running production pipelines at scale.
- Experience evaluating and negotiating with data vendors with a view on cost versus value.
Nice to have
- Built or ran a data vendor business or owned a comparable data product end to end.
- Background in a trading firm, hedge fund or other market-facing environment.
Benefits
- Competitive salary and eligibility for equity
- Biannual bonus scheme
- Fully expensed tech to match your needs
- Private health insurance
- Breakfast and dinner allowance for office-based employees
As we hire globally, benefits may vary by location.
Originally posted on Himalayas