Lead Data Engineer - PySpark/Databricks AI Platform

JPMorgan Chase & Co.

Houston (TX)

On-site

USD 115,000 - 170,000

Full time

14 days+

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Job summary

JPMorganChase is seeking a Lead Software Engineer in the Corporate Technology Sector to provide technical leadership in building a trusted Global Know Your Customer (KYC) and Risk Assessment Data Platform. You will guide an agile data engineering team to deliver scalable, secure software across portfolios and collaborate with cross‑functional partners.

The role emphasizes architecture definition, engineering practices, and delivering high‑impact data‑driven solutions, with hands-on leadership

Qualifications

Formal training or certification on software engineering concepts and 5+ years applied experience.
Hands-on practical experience delivering system design, application development, testing, and operational stability at enterprise scale.
Hands-on experience designing and deploying production AI/ML systems, including LLM-based applications and agentic architectures with tool use, memory, and multi-step reasoning in regulated environments.
Expert in one or more programming languages, particularly Python and/or Java
Advanced knowledge of software application development and technical processes, with considerable depth in one or more disciplines (e.g., cloud, AI/ML, data engineering)
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.
Experience in large-scale data processing, microservices, API design, Kafka, Redis, Memcached, observability tools (Dynatrace, Splunk, Grafana), and orchestration frameworks (Airflow, Temporal)
Advanced working knowledge of relational and NoSQL databases, vector stores, data lake architectures, and data governance
Practical cloud-native experience (AWS, Azure, or GCP)
Ability to present and effectively communicate with senior leaders and executives

Responsibilities

Develops secure, high-quality production code for data-intensive applications and platforms, and reviews and mentors other engineers
Creates durable, reusable software frameworks and patterns that are leveraged across teams and functions
Designs and governs agentic Artificial Intelligence, systems, including multi-agent workflows, tool-use integrations, and human-in-the-loop controls appropriate for regulated financial services environments
Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Establishes engineering standards for Large Language Model-based applications — RAG pipelines, embedding workflows, vector store integrations, and model serving — ensuring safety, observability, and reproducibility at scale
Drives adoption of advanced technical methods and practices aligned with the latest industry standards and product development methodologies
Advises cross-functional teams on technological matters within your domain of expertise
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 at scale.