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
  
  

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

Skills Required
snowflake , Data Governance, Apis, Sql, dbt, Databricks, Python, Data Pipeline