Lead Software Engineer- Python / Pyspark / Java / BigData / Data Modernization / AI

hackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this role.

About the role

As a Lead Software Engineer at JPMorganChase within the Asset and Wealth Management- Global Prime Brokerage 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 will define and enforce an AI-driven data product lifecycle, build human-in-the-loop autonomous agents, and lead the modernization of the strategic data mesh. You will apply your depth of knowledge to all aspects of the analytics development lifecycle and partner with stakeholders across product, platform, risk, and domain teams.

Responsibilities

• Collaborate with business and technology teams to develop AI-first analytics and data product solutions
• Define and enforce architecture for an AI-driven data product lifecycle: semantic extraction/alignment, automated lineage and data quality, pipeline code generation, mesh registration, and self-service 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 human-in-the-loop controls
• Design analytics platforms capable of running reporting and other analytics; explore innovative ideas by building real-time 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 to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support)
• Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities
• 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 data quality by design across critical domains and regulatory reporting, improving auditability and control posture

Requirements

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Proven leadership delivering AI-first 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 hands-on 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 human-in-the-loop controls
• Governance proficiency across lineage, data quality, and access control with evidence generation aligned to regulatory expectations (e.g., BCBS 239-class 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 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

Preferred Qualifications

• Python and Java

Benefits

• Competitive total rewards package including base salary, commission-based pay, and/or discretionary incentive compensation
• Comprehensive health care coverage
• On-site health and wellness centers
• Retirement savings plan
• Backup childcare
• Tuition reimbursement
• Mental health support
• Financial coaching

About Us

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses, and many of the world's most prominent corporate, institutional, and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years, and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing, and asset management.

About The Team

J.P. Morgan Asset & Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors, and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families, and foundations take a more intentional approach to their wealth or finances to better define, focus, and realize their goals.

Our Asset and Wealth Management division is driven by innovators who create technology solutions that make us work more efficiently and help our businesses grow. It's our mission to efficiently take care of our clients' wealth, helping them get and remain properly invested. Our team of agile technologists thrives in a cloud-native environment that values continuous learning using a data-centric approach in developing innovative technology solutions.