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Software Engineer II - Platform Engineer/Databricks
mid · Technology / Software Development
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company's data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly.
As a Software Engineer at JPMorgan Chase within the Chief Data Analytics Office - AIML Data Platforms Team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Solves the companies most challenging cloud data platform problems by building innovative technical solutions around Data Lake tools
- Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
- Designs, implements, and maintains a managed AWS Databricks platform, and provides engineering and operational support for the platform to SRE and app teams.
- Performs platform design, set-up and configuration, workspace administration, resource monitoring, providing engineering support to data engineering teams, Data Science/ML, and Application/integration teams.
- 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
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 2+ years applied experience
- Hands-on practical experience in system design, application development, testing, and operational stability
- Hands-on experience with Python and/or Java application program development with use of automated unit testing
- Hands-on experience with AWS services including provisioning infrastructure using automated tools
- Hands-on experience with GitHub / Bitbucket code versioning tool, Jenkins build tool and pypi / maven artifactory integrations
- Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
- Knowledge of Big Data distributed compute frameworks like Spark and Terraform
- 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.
Preferred qualifications, capabilities, and skills
- Hands-on experience with Big Data Spark Data Pipeline development
- Hands-on experience with Terraform Enterprise Infrastructure provisioning
- Hands-on experience with Databricks
- Knowledge about Platform Administration, Monitoring and Resiliency