# Product Manager, Data Ingestion & Quality

**Company:** [Protege](https://hotfix.jobs/companies/protege)
**Location:** Remote
**Role:** Product Management
**Experience:** 4+ years
**Skills:** SQL, Data Pipelines, Data Ingestion, Data Quality, Metadata Generation, Data Validation, Schema Management, Data Governance, dbt, Great Expectations, Data Contracts, De-Identification, Ml Training Pipelines
**Posted:** 2026-07-07

> Product Manager owning the data ingestion and quality platform at Protege, turning raw partner data into trustworthy, catalog-ready assets via validation gates, metadata pipelines, QA standards, and de-identification. Requires 4-7 years PM experience on data pipelines/ingestion with hands-on SQL and raw data debugging skills.

## Job Description

## 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.

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