Designs, develops, operates, and scales secure data platforms, backend systems, pipelines, and distributed services. Provides technical leadership, reviews code, influences architecture, and serves as a subject matter expert in data processing, storage, and reliability. Leads enterprise adoption of AI-assisted engineering and SDLC automation by establishing governance, security controls, quality gates, and measurable outcomes. Collaborates across global teams and promotes scalable engineering practices and technologies.
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)
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