VP, Lead Analytics Engineer

Bring your expertise to JPMorgan Chase. As part of Risk Management and Compliance (RM&C), you are at the center of keeping JPMorgan Chase strong and resilient. You help the firm grow its business in a responsible way by anticipating new and emerging risks, and using your expert judgement to solve real-world challenges that impact our company, customers and communities.  Our culture in Risk Management and Compliance is all about challenging the status quo and striving to be best in class.

As a Lead Analytics Engineer within the RM&C Data Analytics & Intelligent Automation team, you’ll execute our analytics engineering roadmap to enable cloud-native BI and analytics. Databricks is the primary platform; you’ll design the end-to-end Lakehouse stack—from ingestion to governed semantic models and self-service consumption—leveraging Delta Lake, Unity Catalog, Delta Live Tables (DLT), Databricks SQL, and Databricks Genie. Tools such as Alteryx, Prophecy, Snowflake, Tableau, and Sigma may be used within the solutions you establish.

Success is measured not only by technical delivery, but by adoption and usability. You’ll partner closely with business users, negotiate trade-offs, and translate complex risk data into clear executive narratives.

Job Responsibilities

• Architect and implement end-to-end pipelines on Databricks using DLT, Delta Lake, and Unity Catalog for governance, lineage, and access controls.
• Build ingestion/integration pipelines in Python/PySpark, consuming JPMC’s data products into the Lakehouse; use Alteryx/Prophecy as feeder tools where appropriate.
• Design a governed self-service layer in Databricks SQL—curated semantic models, intuitive views, and controlled exposure—to enable independent exploration by business teams.
• Ensure proper data governance in partnership with data product owners and data architects, including cataloging, lineage, ownership, and entitlements.
• Advise on cloud analytics strategies including onboarding new Databricks users and citizen developers—defining enablement patterns, required entitlements, technical requirements, and governance.
• Communicate clearly with senior, non-technical stakeholders; turn data into actionable narratives and decisions.
• Deliver executive-ready dashboards and self-service analytics in Tableau and/or Sigma on Databricks-served data.

• Use AI tooling, such as GitHub Copilot, to accelerate development while maintaining independent technical ownership and validating all AI-generated code/documentation.

   

Required Qualifications, Capabilities & Skills

• Degree/certification in Computer Science, Information Systems, Data Engineering, or related field, with 7–8+ years in data/analytics engineering or BI in a regulated environment.
• Hands-on, independent proficiency in:
  • ETL/pipeline design, development, testing, and operations
  • Advanced SQL, including writing, optimization, and troubleshooting
  • Python and PySpark, with production-grade implementation experience
  • Databricks Lakehouse architecture, including pipelines, Delta Lake, and Unity Catalog
  • Enterprise BI/visualization with Tableau and/or Sigma
• Strong schema/modeling experience for integrated datasets; practical knowledge of data mesh concepts.
• Strong written and verbal communication for senior audiences.
• Comfortable operating in controlled environments, including change management, access controls, Git, Jules, and ServiceNow.
• Demonstrated customer-centric delivery: requirements negotiation, adoption enablement, and building self-service/citizen-developer platforms.
• Experience designing semantic layers / governed BI models, ideally with Databricks SQL.

• Demonstrated experience implementing a Databricks strategy in an enterprise environment, including operating model, governance, onboarding/enablement, and scalable adoption patterns.

   

Preferred Qualifications, Capabilities & Skills

• Experience with a secondary platform: Snowflake, Redshift, Oracle, SQL Server, or PostgreSQL.
• Hands-on Alteryx and/or Prophecy experience.
• AWS experience, especially supporting Databricks.
• Prior experience supporting Risk, Compliance, Finance, or Audit functions.
• Experience shaping platform strategy, defining standards, or leading an analytics engineering CoE.