Technical Product Manager, Data Ingestion & Quality

About the role

Protege is seeking a Technical Product Manager to oversee the data ingestion and quality pipeline. This role focuses on transforming raw partner data into a reliable, catalog-ready asset for AI training. You will establish the foundational infrastructure that ensures data integrity across various industries.

Key facts

What you'll do

• Define and manage the product roadmap for the data ingestion pipeline, including validation gates and quality checks from initial data arrival to catalog readiness.
• Make product decisions regarding metadata extraction and generation during ingestion, such as transcripts, tags, confidence scores, and schema inference.
• Establish and enforce "catalog-ready" standards, developing tools to ensure compliance and directly validating data quality through queries and pipeline output reviews.
• Collaborate with vertical stakeholders to translate specific data readiness requirements into consistent, platform-level standards that avoid custom engineering solutions.
• Write SQL queries, review pipeline outputs, and define quality benchmarks at each stage of the ingestion process.

Requirements

• 4-7 years of product management experience with a core focus on data pipelines, data quality systems, or data ingestion platforms.
• Demonstrated technical depth, including the ability to write SQL, interpret pipeline logs, identify schema mismatches, and understand data validation architecture trade-offs.
• Experience managing products that ingest messy, inconsistent external data from third-party partners and transforming it into trustworthy assets.
• Proven judgment in build-versus-partner decisions within a rapidly evolving technical environment, evaluating tools and structuring flexible vendor relationships.
• Ability to engage in both technical and product-level conversations, building credibility across engineering teams and vertical product managers.

Nice to have

• Experience with data quality frameworks, metadata standards, or catalog tooling (e.g., dbt, Great Expectations, data contracts).
• Familiarity with de-identification methods for sensitive data like PHI, PII, or confidential enterprise information.
• Background in healthcare data operations, financial data infrastructure, or other domains where data quality has significant downstream impacts.
• Exposure to machine learning training pipelines or AI data workflows where data fitness influences model outcomes.
• Experience with data governance strategies.

Skills & tools

• SQL
• Data pipeline management
• Data quality systems
• Metadata generation
• Schema inference
• dbt (nice to have)
• Great Expectations (nice to have)
• Data contracts (nice to have)

Practical notes

This role is not for individuals primarily focused on customer-facing data products like analytics dashboards or BI tools. The emphasis is on pipeline and infrastructure, requiring direct engagement with raw data to troubleshoot outputs.