Lead Software Engineer Platform Engineering Databricks

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

The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firms data and analytics journey. This includes ensuring the quality integrity and security of the companys data as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutionsthat support the firms commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products improve productivity and enhance risk management effectively and responsibly.

As a Lead Software Engineer at JPMorganChase within the Chief Data Analytics Office - AIML Data Platforms Team youare 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 firms 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 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.

Required qualifications capabilities and skills

• 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

Preferred qualifications capabilities and skills

• 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.