Job description
As a Software Engineer II at JPMorgan Chase within Consumer and Community Banking you will design, build, and optimize scalable data pipelines and architectures on AWS, leveraging Data Lake, Snowflake, and distributed processing technologies.
Job Responsibilities
- Design, build, and maintain scalable AWS-based data pipelines and ETL/ELT processes (e.g., S3, Glue, Lambda, Redshift) using Python and Apache Spark (PySpark).
- Architect and implement Data Lake solutions to enable efficient data ingestion, storage, cataloging, and retrieval.
- Integrate, manage, and optimize Snowflake environments for enterprise data warehousing and analytics.
- Develop and optimize distributed data processing workflows in Spark, focusing on performance, reliability, and cost efficiency.
- Partner with AI/ML teams to enable data-driven solutions, including feature engineering support and data enablement for model deployment.
- Ensure data quality, governance, and security across platforms (controls, auditing, compliance) and maintain strong documentation/runbooks.
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
Monitor, troubleshoot, and automate operations for data pipelines and infrastructure; leverage approved AI coding assistants (e.g., Copilot; others subject to JPMorganChase approval) to improve developer productivity and code quality.
Required qualifications, capabilities and skills
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 5+ years of professional experience in data engineering, including hands-on delivery on AWS.
- Strong proficiency with AWS services such as S3, Glue, Lambda, Redshift, and IAM.
- Proven experience designing and implementing Data Lake architectures.
- Expertise in Snowflake data warehousing, including schema design, performance tuning, and security controls.
- Advanced programming skills in Python and SQL, with hands-on Apache Spark experience (PySpark preferred) for large-scale data processing.
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs.
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations.
- Strong knowledge of data governance, security, and compliance best practices, with excellent problem-solving and communication skills.
Preferred qualifications, skills, and capabilities
- Experience with workflow/orchestration tools such as Apache Airflow.
- Exposure to DevOps practices and CI/CD pipelines.
- AWS certification (e.g., AWS Certified Data Analytics, Solutions Architect).
- Experience with real-time data processing and streaming (e.g., Kinesis, Kafka).
- Familiarity with BI tools (e.g., Tableau, Power BI).
This job post has been translated by AI and may contain minor differences or errors.
