As a Lead Software Engineer at JPMorganChase within the AI/ML Data Platforms (AMDP) - Data Governance Engineering team, you will spearhead the development and delivery of core Data capabilities as part of the AMDP platform that drive firm-wide innovation and excellence. In this role you are part of an agile team that works to enhance, design, and deliver the software components of the firm’s state-of-the-art technology products in a secure, stable, and scalable way.
Job Description
- Executes end-to-end software delivery including design, development, testing, deployment, and technical troubleshooting for Java & Python based services, batch jobs, and data pipelines
- Has strong proficiency in data processing, APIs services, scripting, and automation using common libraries and frameworks relevant to the role. Attention to performance, reliability, and testing discipline
- Has familiarity with modern front-end technologies, especially in React.JS & Angular
- Produces design artifacts for complex workflows covering pipeline orchestration, processing logic, dependency and failure handling, as well as in service-level objectives
- Gathers, analyzes, and synthesizes data to produce reports, metrics, and visualizations that support business and operational insights
- Identifies hidden problems and patterns in data and pipeline execution — including data quality issues, drift, anomalies, and bottlenecks — to drive system improvements
- Works individually or as part of a distributed agile team to deliver projects on time with strong ownership and accountability. Flexibility to work with a global team and multiple time-zones
- Contribute to engineering communities, improve internal libraries and utilities, and evaluate emerging technologies where appropriate
- Leverages enterprise-authorized AI-assisted software development tools to improve code quality, delivery speed, and productivity, while validating outputs through peer review, automated testing, and secure coding standards
- Applies knowledge of tools within the Software Development Life Cycle toolchain, Agile methodology and automation capabilities, to improve delivery speed and the value realized by automation
Required qualifications, capabilities, and skills
- 5+ years of experience with formal training or certification on software engineering concepts.
- Hands-on experience in system design, application development, testing, and operational stability in production environments
- Full stack development experience with Spring, Java, Python, Hibernate, Spark/PySpark, AWS cloud technologies, and Databricks
- Experience with relational databases and SQL; familiarity with NoSQL databases and data modeling concepts
- Ability to learn other coding languages and technologies as needed, with enthusiasm for continuous growth in both technical aptitude and business understanding
- Knowledge across the data lifecycle including ingestion, validation, transformation, storage, governance and lineage considerations, and consumption patterns
- Ability to develop, debug, and maintain code in large enterprise environments with established controls and standards
- Familiarity with continuous integration and delivery, application resiliency, security best practices, and observability and monitoring
- Hands-on experience using enterprise-authorized AI-assisted software development tools 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 and outputs, and adherence to resiliency and security expectations
Preferred qualifications, capabilities, and skills
- AWS Solutions Architect/Developer or advanced-level certification.
- Proficiency across the data management lifecycle.
- Skilled in resolving code issues and proficient in Git for managing repositories and team collaboration.