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

Tools in this posting

SQL
Databricks
Docker
NoSQL
Oracle
Kubernetes

Source

- Updates logical or physical data models based on new use cases - Frequently uses SQL and understands NoSQL databases and their niche in the marketplace - Applies reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines (e.g., test generation and control validation), ensuring traceability/auditability and alignment to resiliency and security expectations.

- Experience across the data lifecycle with hands-on experience in building and optimizing ETL/ELT pipelines (performance tuning, partitioning, file sizing, and incremental loads) - Proficiency in SQL, including advanced techniques such as joins, analytics and window functions, and query optimization - Experience working with Databricks Genie Spaces and ThoughtSpot integration to Databricks

- Proficiency in SQL, including advanced techniques such as joins, analytics and window functions, and query optimization - Experience working with Databricks Genie Spaces and ThoughtSpot integration to Databricks - Strong Oracle experience, including DDL/DML and schema design best practices, and Oracle performance tuning (execution plans, indexes, partitioning, and statistics)

- Understanding of Datalake , Datamesh and Lakehouse architectures - Exposure to containerization and orchestration technologies such as Docker or Kubernetes - Familiarity with CI/CD tooling and DevOps practices

- Experience working with Databricks Genie Spaces and ThoughtSpot integration to Databricks - Strong Oracle experience, including DDL/DML and schema design best practices, and Oracle performance tuning (execution plans, indexes, partitioning, and statistics) - Experience handling JSON and semi-structured data, including parsing, flattening, and schema evolution considerations

Job description

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Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.

As a Data Engineer III at JPMorganChase within the Corporate Technology , you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

Supports review of controls to ensure sufficient protection of enterprise data
Uses enterprise-authorized AI capabilities within the work environment to accelerate data pipeline/design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements.
Responsible for making configuration and customization changes to generate a product at business or customer requests and advising colleagues in requests
Updates logical or physical data models based on new use cases
Frequently uses SQL and understands NoSQL databases and their niche in the marketplace
Applies reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines (e.g., test generation and control validation), ensuring traceability/auditability and alignment to resiliency and security expectations.

Required qualifications, capabilities, and skills

Formal training or certification on data engineering concepts and 3+ years applied experience in data engineering, with a track record of developing and deploying business critical pipelines in production.
Experience across the data lifecycle with hands-on experience in building and optimizing ETL/ELT pipelines (performance tuning, partitioning, file sizing, and incremental loads)
Proficiency in SQL, including advanced techniques such as joins, analytics and window functions, and query optimization
Experience working with Databricks Genie Spaces and ThoughtSpot integration to Databricks
Strong Oracle experience, including DDL/DML and schema design best practices, and Oracle performance tuning (execution plans, indexes, partitioning, and statistics)
Experience handling JSON and semi-structured data, including parsing, flattening, and schema evolution considerations
Proficiency with Git-based workflows using Bitbucket and/or GitHub, with comfort working in IntelliJ IDEA or similar integrated development environments
Strong problem-solving and debugging skills across ingestion, transformation, and serving layers
Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.
Ability to review and validate AI-assisted outputs (e.g., query suggestions, test ideas, or model change summaries) before use, escalating when uncertain and following data handling requirements.
Experience with statistical data analysis and able to determine appropriate tools and data patterns to perform analysis and customizing changes in tools to generate product outputs

Preferred qualifications, capabilities, and skills

Experience enabling or administering Unity Catalog, including catalog and schema design, permissions management, and lineage and metadata patterns
Understanding of Datalake , Datamesh and Lakehouse architectures
Exposure to containerization and orchestration technologies such as Docker or Kubernetes
Familiarity with CI/CD tooling and DevOps practices
Knowledge of financial services technology or regulated industry environments