Lead Data Engineer
Lead Data Engineer at JPMorgan Chase in London will design and deliver secure, scalable data pipelines and architectures across multiple business functions. The role focuses on testing, maintaining critical data workflows, ensuring enterprise data controls, and leveraging AI tools for accelerated pipeline design and documentation.
Skills required
About this role
Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.
As a Data Engineer III at JPMorganChase within the [insert LOB or sub LOB], you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Supports review of controls to ensure sufficient protection of enterprise data
- Uses enterprise-authorized AI capabilities within the work environment to accelerate data pipeline/design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements.
- Responsible for making configuration and customization changes to generate a product at business or customer requests and advising colleagues in requests
- Updates logical or physical data models based on new use cases
- Frequently uses SQL and understands NoSQL databases and their niche in the marketplace
- Applies reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines (e.g., test generation and control validation), ensuring traceability/auditability and alignment to resiliency and security expectations.
Required qualifications, capabilities, and skills
- A. Formal training or certification on data engineering concepts and 3+ years applied experience (NAMR/APAC – India/LATAM/Hong Kong)
B. Formal training or certification on data engineering concepts and advanced applied experience (EMEA/LATAM-Brazil)
C. Singapore follow local country guidance - Experience across the data lifecycle
- 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., query suggestions, test ideas, or model change summaries) before use, escalating when uncertain and following data handling requirements.
- Advanced SQL skills (e.g., joins and aggregations) and working understanding of NoSQL databases
- Significant experience with statistical data analysis and able to determine appropriate tools and data patterns to perform analysis
- Experience customizing changes in tools to generate product outputs
Preferred qualifications, capabilities, and skills
