Lead Software Engineer - Data

JPMorgan Chase & Co.•Columbus, OH

5h•$152,000 - $215,000

About The Position

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorganChase within the Corporate Technology team, you will design, develop, and deliver scalable, consumer-aligned data products that power executive reporting, analytics, AI and business applications. This role is ideal for an experienced software engineer who has a deep expertise in data development and is passionate about transforming complex enterprise data into trusted reusable products. The ideal candidate has extensive experience with Databricks, SQL, PySpark, Oracle and modern data engineering practices. They will partner with the business stake holders, analytics teams, UI developers, and product owners to build high quality data products that enable self-service analytics, dashboards, AI solutions, and operational reporting.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
8+ years in software engineering and/or data application development.
Expert-level SQL development experience, including query tuning and performance optimization.
Extensive experience with Databricks and the Databricks Lakehouse Platform.
Strong experience building data applications and pipelines using PySpark.
Experience with Oracle databases and enterprise data architecture / integration patterns.
Proven ability to design scalable, reusable data products for business consumers, grounded in strong data modeling (including dimensional modeling).
Experience with API integrations and consuming/combining data from multiple enterprise sources.
Operational excellence and modern delivery practices: incident response (triage/rollback/mitigation/postmortems), data lineage & impact analysis mindset, Agile methodologies, CI/CD, and strong analytical/communication/problem-solving 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

Nice To Haves

Experience building executive dashboards using Tableau, Qlik, or Power BI.
Experience supporting AI, machine learning, or Generative AI initiatives.
Experience with automation technologies and workflow orchestration.
Experience designing semantic layers or enterprise data products.
Familiarity with cloud platforms such as AWS or Azure.
Experience mentoring engineers and leading technical solution design.

Responsibilities

Design, develop, and maintain consumer-aligned data products that support enterprise reporting analytics
Develop curated datasets (Delta Lake tables) and analytics-ready models to support reporting, dashboards, and decision-making.
Drives 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.
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.
Design efficient, maintainable SQL queries, views, stored procedures, and reusable data models
Build reusable data assets that support dashboards, AI use cases, automation, and business applications
Ensure data quality, governance, lineage, and documentation are incorporated into all solutions
Optimize data processing performance and identify opportunity to improve scalability and efficiency
Collaborate closely with frontend developers, analytics engineers, UX Designers and product owners
Stay current on emerging technologies and recommend improvements in cloud data architecture, AI, and automation