Director of Data Engineering

Overview

As a Principal Software Engineer in JPMorgan Chases Corporate Sector, you will lead across multiple teams to design scalable data platforms for KYC and risk. You’ll set architecture, engineering standards, and high-impact software delivery in a fast-moving, regulated environment. You’ll drive AI-enabled engineering practices and secure, observable, reusable solutions that support the firm’s portfolios. This is a senior, hands-on role that shapes technical strategy and execution at scale.

Responsibilities

• Architect and implement scalable engineering frameworks and solutions using modern software design principles
• Develop secure, high-quality production code for data-intensive applications and mentor engineers
• Create durable software frameworks and reusable patterns used across teams
• Design and govern agentic AI systems, including multi-agent workflows and human-in-the-loop controls for regulated environments
• Establish engineering standards for LLM-based applications (RAG pipelines, embeddings, vector stores, model serving) with safety and observability at scale
• Drive adoption of advanced methods aligned with industry standards and product development methodologies
• Serve as SME in data engineering, platform architecture, or AI systems and advise cross-functional teams
• Influence senior stakeholders on technical strategy and direction
• Architect and govern AI-enabled engineering workflows with guardrails for validation, security, resiliency, and reuse
• Leverage SDLC tools and AI-assisted development to improve automation and value at scale

Key requirements

• Hands-on experience delivering system design, application development, testing, and operational stability at enterprise scale
• Experience designing and deploying production AI/ML systems, including LLM-based apps in regulated environments
• Expert in one or more programming languages, particularly Python and/or Java
• Deep knowledge of software development with cloud, AI/ML, or data engineering
• Experience with large-scale data processing, microservices, API design, Kafka, Redis, MemCached, observability tools, and orchestration (Airflow, Temporal)
• Advanced 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 communicate with senior leaders
• Experience designing and leading adoption of agentic AI-enabled development practices with human-in-the-loop validation and secure data handling
• Strong understanding of responsible AI use and governance to influence safe scaling patterns

• strong communication
• leadership and influence
• cross-functional collaboration
• Python
• Java
• AWS/Azure/GCP cloud

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