Lead Analytics Engineer – Risk & Compliance Platform

JP Morgan Chase

Newark (DE)

On-site

USD 140,000 - 200,000

Full time

12 days ago

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Job summary

JPMorgan Chase is seeking a Lead Analytics Engineer in RM&C Data Analytics & Intelligent Automation to design and implement scalable Databricks-based pipelines and governed semantic models. You will enable self-service analytics and executive reporting for risk and compliance stakeholders.

You will collaborate with business users, define enablement patterns, and ensure data governance across the Lakehouse. The role emphasizes adoption, governance, and delivering insights to senior leadership.

Qualifications

Degree in Computer Science, Information Systems, Data Engineering, or related field; 7–8+ years in data/analytics engineering or BI in 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; knowledge of data mesh concepts.
Strong written and verbal communication for senior audiences.

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 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.