Be an integral part of an agile team that's constantly pushing the envelope to innovate, build, enhance and deliver top-notch technology products.
As a Lead Data Engineer at JPMorgan Chase within the GIB and a part of the Capital Markets Team you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Lead a Squad of Data Engineers to meet business deliveries working with Product and Design leads.
- Lead Agile ceremonies including standups, retros and technical refinements.
- Self-starter able to take the initiative and shape their own path and a pragmatic and iterative approach to achieving our long-term goals
- 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.
- Provide frequent updates to senior stakeholders on progress of business deliveries.
- 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.
- Use domain modelling techniques to allow us to build best in class business products.
- Structure software so that it is easy to understand, test and evolve.
- Promptly investigate and fix issues and ensure they do not resurface in the future.
- Own and deliver end-to-end, scalable, and secure solutions in the form of cloud-native microservice architecture applications, leveraging modern technologies and the best industry practices.
- Investigate and fix issues promptly and ensure they do not resurface in the future.
- Make sure our releases happen with zero downtime for our end-users.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 3 years applied experience.
Good working knowledge of AWS, Databricks, and Python.
Experience across the data lifecycle.
Advanced at SQL, including joins and aggregations.
Working understanding of NoSQL databases.
Significant experience with statistical data analysis and ability to determine appropriate tools and data patterns for analysis.
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., model/design summaries or operational checklists) before use, escalating when uncertain and following data handling requirements.
Knowledge of modern software architecture patterns.
Experience with a modern CI/CD platforms such Circle Ci/Jenkins.
Experience with modern version control platform such as GitHub/Bitbucket.
Preferred qualifications, capabilities, and skills
Familiarity with the Standardized data layer practises (Medallion architecture)
Exposure to Aurora Postgres and MongoDB
Skills in designing efficient data models including normalization, denormalization, and schema design and an understanding around relational and star schemas.