Analytics Solutions - Vice President

JPMorgan Chase JPMorgan Chase · Banking · Chicago, IL +1 · Commercial & Investment Bank

This role focuses on building governed analytical foundations by formalizing product-level data semantics across a complex, heterogeneous landscape. The goal is to create machine-navigable data foundations that support cross-product analysis, AI-mediated consumption, and operational decision-making. The role involves discovering and formalizing data semantics, determining integration needs, building reusable patterns, embedding governance, and partnering with stakeholders.

What you'd actually do

  1. Systematically discover and formalize product-level data semantics — entities, identifiers, lifecycle stages, metrics, and system touchpoints — using documentation, schemas, and SME validation
  2. Determine where structural data integration is required versus where documented semantic alignment is sufficient, and develop reusable principles for applying that judgment
  3. Build reusable patterns for identifier linkage, lifecycle mapping, and metric definition that reduce onboarding effort as more products are formalized
  4. Embed governance in the assets: produce versioned definitions with lineage, ownership, confidence, and known limitations; enforce validation gates before release
  5. Partner with Data Owners, Data Stewards, and Technology teams to validate definitions and drive approvals through the governance cadence

Skills

Required

  • 7+ years in data analytics, data strategy, data integration, or management consulting in complex, multi-system environments
  • Demonstrated experience linking data across heterogeneous source systems with non-trivial identifier and semantic challenges
  • Strong SQL proficiency across relational and cloud-native database systems
  • Experience formalizing business metrics with explicit definitions, calculation logic, and documented assumptions
  • Working familiarity with generative AI and LLM capabilities, including strengths, limitations, and the need for validation guardrails
  • Experience producing structured data documentation (dictionaries, lineage, metadata) as a deliberate output of analytical work
  • Strong communication skills and stakeholder partnership capability

Nice to have

  • Financial services or similarly complex regulated industry experience
  • Experience with modern cloud data platforms, particularly Snowflake and/or AWS-based services
  • Familiarity with semantic layers, data modeling, or knowledge representation methods that formalize meaning across systems
  • Experience operating governance forums and approval cadences for definitions, linkages, and quality thresholds
  • Experience designing reusable onboarding approaches (patterns, templates, validation protocols) intended to scale across domains
  • Comfort with AI-augmented workflows where human judgment validates AI-assisted outputs before they become authoritative
  • Proficiency in Python or R for data transformation, validation, or automation
  • Undergraduate or graduate degree in a quantitative, technical, or information science discipline.

What the JD emphasized

  • formalize product-level data semantics
  • governed analytical foundations
  • machine-navigable data foundations
  • AI-mediated consumption
  • governed assets