Software Engineer II - Platform Engineer Databricks

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 120,000 - 150,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a Software Engineer II within the Chief Data Analytics Office (CDAO) AIML Data Platforms Team in Jersey City, NJ.

You will contribute as a core technical member, delivering trusted, scalable data platform solutions for multiple business functions. You will design and implement cloud-based data solutions, leverage AI-assisted tools, and collaborate with data engineering, data science, and application teams to enhance risk management, productivity, and

Qualifications

• 2+ years software engineering experience
• Proficient in Python or Java with automated unit testing
• Hands-on experience with AWS services and infrastructure provisioning
• Experience with GitHub/Bitbucket, Jenkins, and package repositories
• Proficient in large corporate environment coding in modern languages and SQL
• Knowledge of Spark and Terraform
• Experience with AI-assisted software development tools and secure coding
• Understanding of responsible AI practices in engineering workflows

Responsibilities

• Executes software solutions, design, development, and debugging with ability to tackle non-routine problems
• Solves cloud data platform challenges by building innovative Data Lake solutions
• Identifies hidden data patterns to improve code quality and system design
• Designs and maintains a managed AWS Databricks platform for data teams and apps
• Configures platforms, administers workspaces, monitors resources, and supports data teams
• Utilizes enterprise AI coding tools to boost quality, speed, and productivity with peer review
• Implements SDLC tooling and automation to increase value of software delivery

Skills

Python

Java

Cloud computing

Data engineering

Testing

Tools

AWS

GitHub

Bitbucket

Jenkins

Artifactory

Pip/Maven

Databricks

Terraform

Spark

Job description

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 solutionsthat 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 II at JPMorgan Chase within the Chief Data Analytics Office - AIML Data Platforms Team, youare 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 company's 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 of Platform Administration, Monitoring and Resiliency