Product Manager, Data Ingestion & Quality

Responsibilities

• Own the supply side of the data platform, including the ingestion pipeline that transforms raw partner data into catalog-ready, trustworthy data.
• Define stages, validation gates, quality checks, and metadata generation for the ingestion pipeline to make it repeatable across modalities and verticals.
• Own product decisions for metadata extraction/generation (transcripts, tags, confidence scores, schema inference) including thresholds, storage, and surfacing.
• Define QA standards and tooling for "catalog-ready" criteria; get hands-on with data by running queries and reviewing pipeline outputs.
• Translate vertical-specific requirements (healthcare, media, etc.) into consistent platform-level standards without custom engineering per deal.
• Create and own the roadmap for ingestion quality, metadata generation, QA tooling, de-identification workflows, and catalog readiness.
• Review pipeline outputs, quality signals, and ingestion risks with cross-functional partners.
• Write SQL, review logs, spot schema issues, and translate standards into engineering requirements.

Requirements

• 4–7 years of PM experience where the core product was a data pipeline, data quality system, or data ingestion platform (owned the "raw data in, trusted data out" problem).
• Hands-on technical depth: able to write SQL, read pipeline logs, spot schema mismatches, understand data validation architecture tradeoffs; looks at raw data directly to verify, not just metrics.
• Experience with external data: worked on ingesting messy, inconsistently formatted data from third-party partners and making it trustworthy.
• Build-versus-partner judgment: made vendor/tool decisions in fast-moving technical domains, evaluating against changing requirements while preserving flexibility.
• Cross-functional credibility: can write requirements for multiple engineering teams and vertical PMs; hold both technical and product conversations.

Nice-to-Haves

• Experience with data quality frameworks, metadata standards, or catalog tooling (dbt, Great Expectations, data contracts, etc.).
• Familiarity with de-identification for sensitive data (PHI, PII, confidential enterprise data).
• Background in healthcare data operations, financial data infrastructure, or domains where data quality has major downstream impact.
• Exposure to ML training pipelines or AI data workflows.
• Experience with data governance strategies.

What This Is Not

• Not for those who have primarily owned customer-facing data products (analytics dashboards, BI tooling, data visualization).
• Not a fit if you haven't dug into raw data files to debug pipeline outputs.