Role Overview
We're hiring a Product Manager to own the platforms and pipelines that turn raw, often messy data — from internal systems and external partners alike — into trusted, analytics- and AI-ready assets. This is a horizontal, infrastructure-level role: you own the validation, metadata, quality, and governance layers that other teams build on top of, not a single downstream product.
You'll define what good data means at each stage of the pipeline, translate that into platform requirements engineering can build against, and work hands-on with the data itself — writing SQL, reviewing pipeline output, and validating that standards actually hold up in practice.
What You'll Do
Strategy & Roadmap
- Define the vision, roadmap, and success metrics for the data platform, treating it as a durable strategic asset rather than a one-off project
- Identify high-impact opportunities to improve data trust, efficiency, and downstream decision-making
- Translate ambiguous platform and business problems into structured plans with clear milestones
Pipeline, Quality & Metadata
- Define the stages, validation gates, and quality checks data passes through from ingestion to analytics-ready, and own the platform requirements that make this repeatable across sources, teams, or verticals
- Own decisions on what metadata gets generated at ingestion (schema inference, tags, confidence scores, lineage, etc.), at what threshold, and how it's stored and surfaced
- Define what analytics-ready means, build the tooling that enforces it, and personally validate the data — running queries and reviewing pipeline logs, not just monitoring dashboards
- Improve how metadata is structured and surfaced to support analytics, governance, and AI use cases
Cross-Functional Delivery
- Partner with engineering to turn requirements into scalable, well-architected systems, making clear build-vs-buy and technical tradeoff calls
- Work with domain and business stakeholders to translate their specific what does ready/done mean needs into consistent, reusable platform standards — avoiding one-off custom work per team or deal
- Act as the connective tissue between business, engineering, and governance functions; hold both a technical and a product conversation in the same meeting
Governance, Compliance & Adoption
- Ensure the platform meets enterprise standards for data governance, privacy, and security, including handling of sensitive data
- Drive adoption of documentation, best practices, and data-integrity standards across teams
- Support user enablement (guides, training, communication) and iterate based on feedback and usage data
Required
What We're Looking For
- 3–4 years of PM experience where the core product was a data pipeline, data quality system, or data platform — you've owned the raw data in, trusted data out problem end to end
- Hands-on technical depth: comfortable writing SQL or Python, reading pipeline logs, spotting schema mismatches, and reasoning through data validation/architecture tradeoffs
- Experience with modern data architecture (e.g., lakehouse, data mesh) and the messy realities of ingesting inconsistent data from external or cross-team sources
- Strong cross-functional credibility — able to write requirements multiple engineering teams and stakeholders can build against, and to work closely with technical teams to drive delivery without being their manager
- Solid grasp of product lifecycle management and agile delivery (prioritization, sprint planning, release management)
- Excellent written and verbal communication skills
Nice to Have
- Experience with data quality or metadata frameworks/tooling (dbt, data contracts, catalog tooling)
- Familiarity with de-identification approaches for sensitive data (PHI, PII, confidential enterprise data)
- Background in a domain where data quality has real downstream consequences (healthcare, finance, etc.)
- Exposure to ML training pipelines or AI data workflows
- Experience with data governance strategy
- Familiarity with modern data platforms/tools (e.g., Databricks, Snowflake, APIs, operational dashboards)
Ideal Candidate Profile
- Strong analytical and problem-solving skills, comfortable using data to inform product decisions
- Genuine interest in the infrastructure that powers enterprise data and AI workloads
- Comfortable using AI tools to boost productivity and execution
- Growth mindset — eager to learn from senior PMs and engineers
