Join us to advance your software engineering career while building impactful technology solutions. Grow your skills and make a difference with a collaborative team.
As an Experienced Data Platform Engineer at JPMorgan Chase within the Corporate Technology team, you will be a key member of an agile team, responsible for designing and delivering the trusted, market-leading data platforms and infrastructure that our products and teams depend on, in a secure, stable, and scalable manner.
Job responsibilities
- Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
- Designs, develops, and operates scalable data platform services and pipelines, and produces secure and high-quality production code, while reviewing and debugging code written by others
- Drives decisions that influence data platform architecture, application functionality, and technical operations and processes
- Serves as a function-wide subject matter expert in one or more areas of focus (e.g., distributed data processing, data storage, platform reliability)
- Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
- Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets.
- Influences peers and project decision-makers to consider the use and application of leading-edge data and backend technologies
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 10+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability for data platforms and backend systems
- Advanced proficiency across both Python and Java, with strong general backend engineering experience (e.g., building APIs, services, and distributed systems)
- Strong big data and database skills, including hands-on experience with Spark, Databricks, and/or Data Lake, and building large-scale data pipelines
- Experience designing and operating data platform components like ingestion, processing, storage, and access/serving layers and experience with AWS cloud computing using ECS, EKS, EMR, Lambda, etc.
- Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and control expectations; ability to coach managers/leads and influence leaders on safe scaling patterns.
- Advanced knowledge of software applications and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, distributed data processing, artificial intelligence, machine learning, etc.)
- Ability to tackle design and functionality problems independently with little to no oversight
- Experience in developing, debugging, and maintaining code in a large corporate environment and ability to collaborate well with global teams in geographically distributed locations across time zones
- Self-starter, able to reach out to various team members, users, and partner teams to get solutions delivered.
Preferred qualifications, capabilities, and skills
- AI, ML, Claude, MCP
- Familiar with agile development methodologies (e.g., Scrum) and CI/CD, Applicant Resiliency, and Security
- Experience with data orchestration and workflow tools (e.g., Airflow) and streaming technologies (e.g., Kafka)