Data Products - Product Manager

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 What We're Looking For Required 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