Data Platform Lead

Halluminate•San Francisco, CA

1d•Onsite

About The Position

Halluminate is an applied AI and data company building computer-use and tool-use RL gyms to train AI agents on real financial services work — Excel modeling, pitch decks, and everything investment banking, private equity, and hedge funds rely on. We work with frontier labs, graduated from Y Combinator (S25), and raised a seed round to scale the team. The role Base: $200–275k · Equity: 0.15–0.30% · Location: San Francisco, 5 days in office Own our data function from zero. Every RL environment we ship is built on real-world financial data — you are the person who finds it, licenses it, makes it safe to use, and gets it into the hands of the teams building on it. Ideal for data, engineering, or product leaders who want to build a function rather than inherit one.

Requirements

Experience building a data acquisition, licensing, or ops function — ideally one that did not exist before you arrived.
Strong engineering instincts. You will build pipelines and tooling yourself, not just manage vendors.
Product judgment about what makes data good enough for a given use case, and when to stop.
Comfort operating with no playbook, and startup speed: iterate quick, ask quick, respond quick, make mistakes quick.
Client-facing comfort — you'll work directly with project leads, vendors, and SMEs.

Responsibilities

Stand up the data team from scratch — you are the first hire and the owner.
Source, license, and purchase real-world financial data and artifacts — data rooms, filings, models, decks — from across the industry.
Build the anonymization and compliance pipeline that turns sensitive industry data into training-safe assets.
Build the systems that catalogue, version, and distribute data so project teams can self-serve.
Act as the internal supplier to our SPL project pods, fulfilling requests for data/tokens that are the foundation for our RL environments
Own vendor and data-provider relationships end to end, including negotiation and renewal.
Own data quality, provenance, and licensing terms — and the judgment calls about what we will and will not use.