Lead Software Engineer, Data Engineering & Applied AI

JPMorgan Chase

Confirmed live 2 days ago Trusted

Quick summary

Work type: On-site
Location: Plano, TX
Posted: 25 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $203k

$180k most similar roles pay here $230k

This listing doesn't post a salary. Most similar roles pay $192,050–$214,150.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Lead Software Engineer, Data Engineering & Applied AI

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As a Lead Software Engineer - Data Engineering & Applied AI within the Asset & Wealth Management team, you will serve as a core technical contributor building next-generation data management products including metadata management, lineage, and data quality. You will design and develop an agentic production data platform with full stack ownership while driving the adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed. Your daily work involves developing AI skills, MCP servers, and autonomous agents using Claude Code. You will utilize Python, Java, or Kotlin for backend services, React or Angular with TypeScript for frontends, and Spark, Kafka, Airflow, Snowflake, and AWS for data engineering. The role focuses on solving complex problems in data architecture, modeling, and building scalable batch and streaming integrations within a secure, stable environment.

Skills

AI RAG Python Java Kotlin TypeScript React Angular Spark Kafka Airflow Snowflake AWS CI/CD GraphQL REST Metadata Management SRE

What you'll do

Design and deliver AI-enabled platform capabilities for data management products like quality, metadata, and lineage.
Develop and manage an agentic production data platform with full-stack application ownership.
Implement AI-assisted engineering practices to improve code quality, delivery speed, and automated testing.
Build autonomous agents and MCP servers to enhance product capabilities and deliver business value.
Develop secure, high-quality production code while performing peer reviews and debugging for the team.
Automate the remediation of recurring issues to improve the operational stability of software systems.
Evaluate open-source solutions through rapid POCs and lead the hardening process for enterprise use.
Build workflow orchestration services and scalable batch or streaming integrations for data pipelines.

What we're looking for

Formal training or certification in Software Engineering and 5+ years of applied experience.
Bachelor's degree required; advanced degree preferred.
Demonstrated experience leading the use of approved AI-assisted software development tools for coding, review, testing, and troubleshooting.
Strong understanding of responsible AI practices, including data sensitivity, secure handling of inputs/outputs, and coaching others on compliant adoption.
Strong knowledge of data architecture and modeling patterns, including dimensional modeling and database design.
Experience in end-to-end delivery of data management products across UI, APIs, services, integrations, and data pipelines.
Ability to evaluate open-source solutions through POCs and lead selection, customization, and enterprise hardening for reliability and security.
Experience with financial services or wealth management, data governance, React/Angular, Python/Java/Kotlin, Spark, Kafka, Airflow, Snowflake, AWS, and LLM technologies (preferred).

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