JP Morgan Chase

Runs a consumer bank and an investment bank

Lead Software Engineer: Data Engineering

Data EngineeringConfirmed live in the last 24 hours

Full-timeColumbus, OH, USANo salary listedIn-person

About the Job

Requirements

Formal training or certification on data engineering concepts, architecture, and production ownership, with 5+ years of applied experience.
Hands-on practical experience delivering system design, application development, testing, and operational stability.
Strong Python coding skills and deep experience with Databricks and Spark.
Experience leading the effective use of approved artificial intelligence-assisted software development tools for coding, code review, test acceleration, or troubleshooting, including validating outputs for correctness, performance, and security.
Proficiency in all aspects of the Software Development Life Cycle.
Advanced understanding of agile methodologies, continuous integration and continuous delivery, application resiliency, and security.
Strong experience with Amazon Web Services and modern lakehouse and data platform patterns.
Proven technical leadership through architecture decisions, mentoring, and cross-team influence.

Responsibilities

Execute creative software solutions, design, development, and technical troubleshooting to build solutions and break down technical problems.
Develop secure and high-quality production code, and review and debug code written by others.
Architect and deliver data platforms and pipelines across ingestion, transformation, curation, and consumption on Amazon Web Services and Databricks using Spark and Delta.
Build reusable Python and/or Scala libraries, frameworks, pipeline templates, and automation.
Own operational excellence, including data quality, monitoring and alerting, service-level agreements, incident triage and root-cause analysis, and performance and cost optimization.
Drive Software Development Life Cycle standards covering continuous integration and continuous delivery, testing strategy, secure coding, resiliency patterns, and design and code reviews.
Lead and mentor engineers and partner with product, analytics, and security teams to deliver roadmaps and execution.
Use enterprise-authorized generative artificial intelligence developer tools, including GitHub Copilot and Claude Code, to accelerate refactoring, test generation, and documentation with strong validation and controls.
Drive team adoption of enterprise-authorized artificial intelligence-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, while establishing validation standards and promoting reuse of effective patterns.
Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized artificial intelligence-assisted development and automation capabilities, to improve the value realized by automation.
Identify opportunities to eliminate or automate remediation of recurring issues to improve the operational stability of software applications and systems.

Desired Qualifications

Amazon Web Services certification and/or Databricks certification.
Experience with streaming architectures, including event-driven ingestion, near-real-time processing, and operational support for monitoring, alerting, and recovery.

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

As an Engineering Lead at JPMorganChase within Employee Platforms, you are an integral part of an agile team responsible for the hands-on delivery and architecture of secure, stable, scalable data engineering solutions on AWS and Databricks. As a core technical contributor, you own technical direction, develop critical components, drive production reliability, and deliver trusted technology products in support of the firm’s business objectives.

Job responsibilities

Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
Develops secure and high-quality production code, and reviews and debugs code written by others.
Architects and delivers data platforms and pipelines across ingestion, transformation, curation, and consumption on AWS and Databricks using Spark and Delta.
Builds reusable Python and/or Scala libraries, frameworks, pipeline templates, and automation.
Owns operational excellence, including data quality, monitoring and alerting, SLAs, incident triage and RCA, and performance and cost optimization.
Drives SDLC standards covering CI/CD, testing strategy, secure coding, resiliency patterns, and design and code reviews.
Leads and mentors engineers and partners with product, analytics, and security teams to deliver roadmaps and execution.
Uses enterprise-authorized GenAI developer tools such as GitHub Copilot and Claude Code to accelerate refactoring, test generation, and documentation with strong validation and controls.
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.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems

Required qualifications, capabilities, and skills

Formal training or certification on data engineering concepts with architecture and production ownership concepts and 5+ years applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Strong Python coding skills, and deep experience with Databricks and Spark.
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.
Proficient in all aspects of the Software Development Life Cycle
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Strong experience with AWS and modern lakehouse and data platform patterns.
Proven technical leadership through architecture decisions, mentoring, and cross-team influence.

Preferred qualifications, capabilities, and skills

AWS certification and/or Databricks certification.
Experience with streaming architectures, including event-driven ingestion, near-real-time processing, and operational support for monitoring, alerting, and recovery.

About the Company

JP Morgan Chase

Runs a consumer bank and an investment bank

JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1799

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