Pay: Salary not listed in the saved posting
Work setup: Unconfirmed
Employment: Unconfirmed

Tools in this posting

Python
SQL
AWS
Databricks
Delta
Sigma
Snowflake
Tableau
PySpark

Source

- 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.

- 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.

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

- 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.

- 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.

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.

- 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.

- 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.

Job description

View original posting ↗

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.