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2 weeks ago

Technical Product Manager, Data Ingestion & Quality

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 22 hours agoStatus: Live

Job Summary

Own the supply side of a privacy-centric AI training data platform, transforming raw partner data into catalog-ready, trustworthy content. Define ingestion validation gates, metadata generation, QA standards, de-identification transformations, and cross-vertical readiness criteria. Work hands-on with SQL and pipeline outputs to ensure quality signals are met, translating data quality gaps into platform requirements engineering can build.

Location: United States

Workplace: Remote

Employment Type: Full time

Job Function: Product Management

Seniority: Mid level

Key Responsibilities

•Own ingestion pipeline stages, validation gates, and quality checks from partner arrival to catalog-ready.
•Own metadata generation decisions at ingestion, including transcripts, tags, confidence scores, schema inference, thresholds, and storage/surfacing.
•Define what “catalog-ready” means and build tooling that enforces QA standards, validating by running queries and reviewing pipeline outputs.
•Translate cross-vertical “ready” requirements into consistent platform-level standards to avoid custom engineering per deal.
•Develop and run a roadmap for improving ingestion quality, QA tooling, de-identification workflows, and catalog readiness.

Key Requirements

•4–7 years of PM experience owning a data pipeline, data quality system, or data ingestion platform (raw data in, trusted data out).
•Hands-on technical depth to write SQL, read pipeline logs, and understand tradeoffs in a data validation architecture.
•Experience with external/third-party data that is messy or inconsistently formatted, and making it trustworthy.
•Make build-versus-partner/vendor decisions in a fast-moving technical domain while preserving flexibility.
•Write requirements across engineering teams and vertical PMs and hold technical and product conversations together.

Experience:4-7 yearsData pipelinesData qualityData ingestion platformsAIHealthcareMedia

Skills:Cross-functional collaborationTechnical communicationProblem-solvingOwning outcomesDecision-making

Tech Stack:SQLDbtGreat Expectations

Company Brief

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Provides a privacy‑first platform that connects data holders with vetted AI developers to license and deliver high‑quality, multimodal training and evaluation datasets across healthcare, media, audio, and motion capture.

Industry: Data Infrastructure

Company Size: Small (11 to 50 employees)

Growth: Growth Stage Startup

Funding: Series A

Headquarters: New York City, United States

Founded: 2024

Website LinkedIn

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Protege

2 weeks ago

Technical Product Manager, Data Ingestion & Quality

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 22 hours agoStatus: Live

Job Summary

Location: United States

Workplace: Remote

Employment Type: Full time

Job Function: Product Management

Seniority: Mid level

Key Responsibilities

•Own ingestion pipeline stages, validation gates, and quality checks from partner arrival to catalog-ready.
•Own metadata generation decisions at ingestion, including transcripts, tags, confidence scores, schema inference, thresholds, and storage/surfacing.
•Define what “catalog-ready” means and build tooling that enforces QA standards, validating by running queries and reviewing pipeline outputs.
•Translate cross-vertical “ready” requirements into consistent platform-level standards to avoid custom engineering per deal.
•Develop and run a roadmap for improving ingestion quality, QA tooling, de-identification workflows, and catalog readiness.

Key Requirements

•4–7 years of PM experience owning a data pipeline, data quality system, or data ingestion platform (raw data in, trusted data out).
•Hands-on technical depth to write SQL, read pipeline logs, and understand tradeoffs in a data validation architecture.
•Experience with external/third-party data that is messy or inconsistently formatted, and making it trustworthy.
•Make build-versus-partner/vendor decisions in a fast-moving technical domain while preserving flexibility.
•Write requirements across engineering teams and vertical PMs and hold technical and product conversations together.

Experience:4-7 yearsData pipelinesData qualityData ingestion platformsAIHealthcareMedia

Skills:Cross-functional collaborationTechnical communicationProblem-solvingOwning outcomesDecision-making

Tech Stack:SQLDbtGreat Expectations

Company Brief

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Industry: Data Infrastructure

Company Size: Small (11 to 50 employees)

Growth: Growth Stage Startup

Funding: Series A

Headquarters: New York City, United States

Founded: 2024

Website LinkedIn