Lead Software Engineer - Platform Engineering Databricks

JPMorgan Chase & Co.•Jersey City, NJ

3d•$152,000 - $215,000

About The Position

As a Lead Software Engineer at JPMorganChase within the Chief Data Analytics Office - AIML Data Platforms 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 are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience.
Hands-on experience with Python and/or Java application program development with use of automated unit testing.
Hands-on experience in Big Data Compute Engines Apache Spark - Core, SQL (Catalyst Framework), Databricks platform, Kubernetes platform.
Experience in designing, developing, or maintaining production-grade cloud solutions in Cloud ecosystems such as Amazon Web Services (VPC, EKS, EFS).
Hands-on practical experience delivering system design, application development, testing, and operational stability. Ability to tackle design and functionality problems independently with little to no oversight.
Hands-on experience with GitHub / Bitbucket SCM, Jenkins, CI/CD tool, Docker, building container image, Terraform and pypi / maven artifactory integrations.
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

Exposure to AWS & Databricks Platform administration.
Experience with Agile development processes, as needed (SCRUM/KANBAN) using JIRA.
Experience in Data pipelines using Spark.
Experience in managing product release lifecycle at enterprise level.

Responsibilities

Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
Solves the companies' most challenging cloud data platform problems by building innovative technical solutions around Data Lake Tools.
Designs, implements, and maintains a managed Apache Spark on Kubernetes, AWS Databricks platform, and provides engineering and operational support for the platform to SRE and app teams.
Performs platform design, set-up and configuration, workspace administration, resource monitoring, providing engineering support to data engineering teams, Data Science/ML, and Application/integration teams.
Develops secure high-quality production code, and reviews and debugs code written by others.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture.
Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies.
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.