Lead Software Engineer- Python Pyspark Java BigData Data Modernization AI

We have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.

As a Lead Software Engineer- Python / Pyspark / Java / BigData / Data Modernization / AI at JPMorganChase within the Asset and Wealth Management- Global Prime Brokerage 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 firms business objectives.

We look for people who are passionate about solving business problems through innovation analytics and an AIfirst engineering mindsetbuilding reusable governed analytical data products and accelerating delivery of regulatory and CEOpriority analytics. You will define and enforce an AI-driven data product lifecycle (semantic alignment automated lineage and data quality pipeline/code generation mesh registration and self-service consumption) and will build human-in-the-loop autonomous agents to detect schema drift propose transformations reconcile semantics triage data incidents and generate governance evidence. Youll be required to apply your depth of knowledge and expertise to all aspects of the analytics development lifecycle and partner continuously with stakeholders across product platform risk and domain teams. You will lead an AIfirst transformation of data engineering and analytics by productizing the data product lifecycle (semantics lineage DQ governance) and building autonomous agents (humanintheloop) that reduce manual toil improve auditability and enable selfservice consumption on the strategic data mesh. The role also owns modernization of the strategic data mesh.

Job responsibilities

• Collaborate with business and technology teams to develop AIfirst analytics and data product solutions
• Define and enforce architecture for an AIdriven data product lifecycle: semantic extraction/alignment automated lineage and DQ pipeline code generation mesh registration and selfservice consumption
• Build and operate autonomous agents for data engineering that detect schema drift propose transformations reconcile semantics triage data incidents and maintain governance evidence under humanintheloop controls
• Design analytics platforms capable of running reporting and other analytics; explore innovative ideas by building realtime and batch analytics solutions
• Establish appropriate monitoring and alerting of solution events related to performance scalability availability and reliability
• Provide technical leadership guidance and direction to other team members; build prototypes for demonstrations for peer groups business partners and senior leaders
• Drive team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality delivery speed and operational outcomes (e.g. AI-assisted code review/refactoring test strategy acceleration incident/root-cause analysis support) while establishing consistent validation standards (secure coding peer review automated testing) and promoting reuse of effective patterns across the team
• Apply 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
• Lead migration and modernization from legacy analytics/reporting stacks to the strategic mesh ecosystem (e.g. Databricks/Iceberg/common services) reducing fragmentation and duplicated data products
• Industrialize entity resolution and parent identification with ML/LLM solutions and standardize analytical product packaging to enable reuse and monetization
• Embed governance lineage and DQ by design across critical domains and regulatory reporting improving auditability and control posture

Required qualifications capabilities and skills

• Formal training or certification on software engineering concepts and 5 years applied experience
• Proven leadership delivering AIfirst analytics and data engineering at scale including productized data mesh patterns semantic layers and analytical data product lifecycle ownership
• Experience developing data ingestion and integration processes sourcing data from multiple platforms and applying data cleansing/transformation rules for analytics-ready datasets
• Deep handson experience with big data and modern data platforms (e.g. Spark Databricks Snowflake Iceberg) and building robust pipelines and data lake/lakehouse frameworks
• Strong programming capability in Python and PySpark or Java with strong CI/CD and containerization practices
• Applied AI expertise in ML pipelines NLP/LLMs and agentic frameworks to build autonomous agents for engineering tasks (schema drift detection semantic reconciliation incident triage governance evidence generation) under humanintheloop controls
• Governance proficiency across lineage data quality and access control with evidence generation aligned to regulatory expectations (e.g. BCBS 239class lineage/DQ)
• Comfortable working in an agile and collaborative environment; strong written and verbal communication skills
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g. for coding code review test acceleration troubleshooting) with the ability to set team expectations for validating AI outputs for correctness performance and security.
• Strong understanding of responsible AI use in engineering workflows including data sensitivity considerations secure handling of inputs/outputs and adherence to resiliency and security expectations; experience coaching engineers on safe compliant adoption within delivery practices

• Proficient in all aspects of the Software Development Life Cycle

• Python and Java