Lead Data Engineer - Python, Databricks, React

Join us as a Lead Data Engineer and help shape the future of data-driven innovation at JPMorganChase. You will collaborate with talented colleagues to deliver impactful solutions that power AI and analytics initiatives across the firm. We value your expertise, encourage growth, and support your journey to make a lasting impact. Experience a culture that celebrates diverse perspectives and fosters continuous learning.

Job Summary:

As a Lead Data Engineer in the Corporate Technology team supporting CIO, Treasury & Corporate Risk Management, you will enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You will maintain critical data pipelines and architectures across multiple technical areas, supporting the firm’s business objectives. Your work will drive innovation, operational excellence, and team success, contributing to a collaborative culture that values your ideas and technical leadership.

Job Responsibilities:

• Make data available for AI and analytics initiatives, working closely with use case owners to define requirements, manage product dependencies, and support agile routines that oversee cross-product data dependencies and prioritize delivery

• Collaborate with business, technology, and operations partners to understand data requests and accelerate provisioning through deployment of "AI for Data"

• Drive adoption of AI-assisted development tools (e.g., Claude Code, Copilot) to accelerate delivery and improve developer productivity

• Partner with business and technology teams to rapidly prototype and deploy analytics and tooling, leveraging AI/ML and innovative approaches

• Implement and manage platform controls, including access, security, and compliance, ensuring all data and AI solutions meet firmwide SDLC standards

• Provide transparency and drive executive visibility into bottlenecks, progress, performance metrics, and adoption tracking in making AI-ready and critical data sources available for innovation

• Identify the lineage and provenance of critical data assets to support governance, regulatory, and business requirements; embed evergreen controls on data flows to improve safety, transparency, and traceability

• Drive insight into areas of efficiency and risk through consolidation and reengineering of data flows

• Lead data quality issue root cause analysis using deep data profiling and advanced analytics techniques, then fix the cause and embed uplifted evergreen controls to prevent future failures

• Develop proactive controls to reduce the time from data quality issue identification to resolution, improving client experience and driving operational efficiency

• Demonstrate control environment improvements and reduction in toil through common tooling and frameworks; uplift the metadata (semantic layer) of existing data to support AI and Natural Language Query (NLQ) usage, accelerate adoption of Mesh data architecture, reduce consumer friction, and deliver data product prototypes

• Uses enterprise-authorized AI capabilities within the work environment to accelerate data platform and model design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements

• Applies reuse-first, AI-assisted practices within delivery and operational routines (e.g., backup/recovery validation and access control review support), ensuring traceability/auditability and alignment to resiliency and security expectations

Required Qualifications, Capabilities, and Skills:

• Formal training or certification on software engineering concepts and applied experience

• Experience and awareness in working within Risk Analytics space; preferred knowledge of corporate bond investment assets and structured credit products such as securitisation (e.g., CLO, CMBS, RMBS, ABS)

• Proven experience integrating AI-assisted development tools (e.g., Claude Code, Copilot, or similar) into engineering workflows

• Experience in strategic or transformational change initiatives, including data governance, data quality, or analytics transformation programs

• Strong technical skills in data profiling, analysis, and data management using modern tools and environments (Python, R, SQL, Spark, DataBricks, cloud platforms)

• Understanding of data lineage concepts and experience with lineage analysis, metadata management, and data cataloguing

• Excellent communication skills with the ability to convey complex technical concepts to diverse audiences, including executive leadership

• Experience with data quality frameworks, including profiling, rule development, issue remediation, and preventative controls

• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity

• Ability to review and validate AI-assisted outputs (e.g., code, model/design summaries or operational checklists) before use, escalating when uncertain and following data handling requirements

• Hands-on practical experience delivering system design, application development, testing, and operational stability

Preferred Qualifications, Capabilities, and Skills:

• Hands-on experience with data lineage tools and techniques, including graph & vector databases and metadata management platforms

• Hands-on experience with LLM Ops, MLOps, and AI/ML platform deployment at scale

• Familiarity with business-led analytics delivery models and rapid prototyping frameworks

• Experience with AI/ML governance, prompt engineering, and integrating AI tools into SDLC

• Experience with AI/ML technologies and their application to data management challenges (e.g., automated data profiling, metadata enrichment)

• Understanding of agile and product management methodologies and experience working in agile teams

• Ability to multi-task in a fast-paced environment and operate independently with minimal supervision; strong judgment with the ability to balance strategic vision with pragmatic, incremental delivery

• Experience building and growing capabilities and developing talent in data science or data management teams

• Excellent interpersonal skills and ability to build strong working relationships with business, technology, and control stakeholders across global teams