Build and maintain Databricks-based data integration and ETL pipelines using Python, PySpark, DLT, Delta Lake, and Unity Catalog. Design governed semantic models and self-service analytics layers, support data governance and access controls, and deliver executive dashboards in Tableau or Sigma. Partner with business stakeholders to translate requirements into actionable insights while maintaining production-quality code, documentation, lineage, and compliance in a regulated environment.
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.
As an Analytics Engineer Senior Associate at JPMorgan Chase within the RM&C Data Analytics & Intelligent Automation team, you’ll support our analytics engineering roadmap to enable cloud-native BI and analytics. You will consume JPMC’s data products, build and maintain integration/collation pipelines using appropriate ETL tooling, contribute to schema design, and produce high-impact dashboards and self-service analytics products that business teams trust and use.
Databricks is the primary platform. You will work across the Lakehouse stack—from ingestion through governed semantic models and self-service consumption—leveraging Delta Lake, Unity Catalog, Delta Live Tables (DLT), Databricks SQL, and Databricks Genie, with tools such as Alteryx, Prophecy, Snowflake, Tableau, and Sigma as part of the overall ecosystem.
Job Responsibilities
- Develop 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.
- Implement 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.
- Communicate clearly with 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 and Skills
- Degree or certification in Computer Science, Information Systems, Data Engineering, or a related discipline.
- 5+ of applied experience in data engineering, analytics engineering, or business intelligence in a complex, regulated environment.
- Hands-on ability to design, write, test, and maintain ETL pipelines independently, with strong SQL fluency and production-grade Python, PySpark experience.
- Strong communication skills and a customer-centric mindset, with the ability to engage business users to gather and clarify requirements and support adoption.
- Experience with Databricks, including building and executing pipelines on the platform.
- Experience with self-service analytics platforms, including working knowledge of semantic layers, BI data models, and governed virtualization for non-technical users.
- Comfort operating in a controlled environment, including change management and access control, with working knowledge of Git, Jules, and ServiceNow.
Preferred Qualifications, Capabilities and Skills
- Hands-on experience with Alteryx and Prophecy for ETL pipeline development.
- Familiarity with AWS cloud environments supporting data workloads.
- Prior experience in Risk Management, Compliance, Finance, or Audit, or demonstrated ability to rapidly develop fluency in risk and regulatory subject matter.
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