Crm Data Product Manager - Vice President

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Commercial & Investment Bank

Product Manager for CRM data products in financial services, focusing on creating governed, reliable data assets (datasets, metrics, dashboards, APIs, ML-ready data) to support advanced analytics and AI/ML use cases. The role involves defining requirements, ensuring data quality, and applying governance and controls.

What you'd actually do

  1. Gather and document business needs, user stories, and acceptance criteria for data products
  2. Translate business questions into clear data requirements (definitions, grain, scope, SLAs)
  3. Maintain and groom the product backlog; help prioritize work with the Product Manager/Lead
  4. Coordinate sprint activities, assist with release planning, and track delivery milestones
  5. Help define and monitor data quality rules (completeness, accuracy, timeliness, consistency)

Skills

Required

  • Product Thinking
  • Databricks/Snowflake
  • Data Products Buildout
  • data governance
  • data quality
  • compliance
  • enabling AI/ML workloads
  • sales domains (deal lifecycle, client coverage, pipeline/execution, fee/revenue analytics)
  • communication
  • outcome-focused
  • hands-on

Nice to have

  • working in a highly matrixed, complex organization

What the JD emphasized

  • governed, reliable data products
  • data contracts
  • SLAs
  • measurable outcomes
  • actionable insights and reusable data assets
  • curated datasets, metrics layers, dashboards, marts, APIs, notebooks, and ML-ready data
  • strategic data initiatives and tooling
  • data governance
  • reporting
  • advanced performance analysis
  • end-to-end lifecycle
  • defining requirements
  • data quality and usability
  • reliable, discoverable, and valuable
  • data quality rules
  • data incidents and defects
  • data product documentation
  • governance and controls
  • stewardship, cataloging, lineage, entitlements
  • protect sensitive data with masking/tokenization/entitlements and regulatory compliance
  • prioritize use cases and communicate outcomes
  • leverage strategic capabilities
  • production delivery
  • data governance, quality, compliance
  • enabling AI/ML workloads
  • sales domains
  • highly matrixed, complex organization