Company Overview: We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data. Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech. We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI. About Protege We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data. Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech. We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI. Role Overview We're hiring a Product Manager to own the supply side of Protege's data platform — the pipeline that takes raw data from a partner and turns it into something catalog-ready, trustworthy, and usable. Right now, that process is manual, inconsistently applied, and a source of delivery risk. Your job is to change that. This is a horizontal platform role, not a vertical one. You own the infrastructure that makes data trustworthy enough to build products from in the first place: the validation gates, the metadata generation pipelines, the QA standards, the de-identification transformations, and the catalog-readiness criteria that let the rest of the organization actually trust what's in our catalog. You'll work across healthcare, media, and any other vertical we enter. You'll write SQL, review pipeline outputs, define what "good" looks like at each stage of ingestion, and translate those standards into platform
requirements that engineering can build against. The supply side is where data quality is won or lost. If this layer isn't working, nothing downstream works. It's foundational, largely invisible to customers, and one of the most important things we can build.
What you'll work on Ingestion pipeline product: define the stages, validation gates, and quality checks that data passes through from partner arrival to catalog-ready; own the platform
requirements that make this repeatable across modalities and verticals Metadata generation: own the product decisions around what metadata gets extracted or generated at ingestion, including transcripts, tags, confidence scores, schema inference, at what threshold, and how it gets stored and surfaced QA standards and tooling: define what "catalog-ready" means, build the tooling that enforces it, and get into the data directly to validate that standards are being met; you’ll run queries and review pipeline outputs, not just read dashboards Cross-vertical consistency: work with vertical stakeholders to translate their "what does ready mean for our vertical"
requirements into consistent platform-level standards that don’t require custom engineering per deal What Success Looks Like 30 days: Ramp : Build a clear understanding of Protege’s current data ingestion workflow, including how raw partner data moves from arrival to catalog-ready. Get hands-on with pipeline outputs, schemas, metadata, validation checks, and QA processes so you understand where quality risk shows up in practice. Build context with engineering, vertical stakeholders, GTM / delivery, and DataLab on where ingestion quality is most manual, inconsistent, or risky today. 60 days: Take Ownership: Own the first clear version of what “catalog-ready” means across the ingestion pipeline, including validation gates, metadata
requirements, QA standards, and readiness criteria. Translate the highest-priority ingestion quality gaps into product
requirements engineering can build against. 90 days: Operate Independently: Own the roadmap for improving ingestion quality, metadata generation, QA tooling, de-identification workflows, and catalog readiness. Create a repeatable operating rhythm for reviewing pipeline outputs, quality signals, and ingestion risks with the right cross-functional partners
What we're looking for 4–7 years of PM experience where the core product was a data pipeline, data quality system, or data ingestion platform — you’ve owned the "raw data in, trusted data out" problem before Hands-on technical depth — you can write SQL, read pipeline logs, spot a schema mismatch, and understand the tradeoffs in a data validation architecture; you look at data directly to verify things are working, not just at metrics Experience with external data — you’ve worked on a product that ingested messy, inconsistently formatted data from third-party partners and had to make it trustworthy; you know what that problem actually feels like Build-versus-partner judgment — you’ve made vendor decisions in a fast-moving technical domain; you know how to evaluate a tool against
requirements that will change, and how to structure relationships that preserve flexibility Cross-functional credibility — you’ll be writing
requirements that multiple engineering teams and vertical PMs depend on; you can hold a technical conversation and a product conversation in the same meeting
Nice to have: Experience with data quality frameworks, metadata standards, or catalog tooling, including dbt, Great Expectations, data contracts, or similar Familiarity with de-identification approaches for sensitive data — PHI, PII, or confidential enterprise data Background in healthcare data operations, financial data infrastructure, or any domain where data quality has real downstream consequences Exposure to ML training pipelines or AI data workflows, where data fitness affects model outcomes Experience with data governance strategies What this is not This is not a role for someone who has primarily owned data products from the customer side — analytics dashboards, BI tooling, or data visualization. The product here is the pipeline and the infrastructure, not the interface on top of it. If you haven't actually dug into raw data files to find out why a pipeline produced the wrong output, this is probably not the right fit. Protege Values Pass the Loved Ones’ Test We act with integrity and do the right thing — especially when it’s hard and no one is watching. Always Find a Way We are resourceful, resilient builders who solve hard problems and push through obstacles. Go Fast and Grow Fast Velocity matters. We move with urgency, learn quickly, and continuously improve as individuals and as a company. Practice Kindness and Candor We communicate directly and respectfully, building trust through honest feedback and genuine care for one another. Deliver Together We win as one team. Collaboration, accountability, and shared ownership drive our success. Own the Outcome. Hone the Craft. We take pride in our work, sweat the details, and continuously raise the bar for excellence.
About Protege
The biggest unmet need in AI today is getting access to the right training data. Data holders often don’t know where to start and are rightly concerned about governance, intellectual property, and security implications. AI companies can spend years finding and negotiating access to the data they need.Protege is solving these problems by providing an easy-to-use platform to connect data holders with vetted data users.
