Vista previa de la oferta
Lead Data Engineer - Python, SQL - Team Lead - Vice President
lead · Technology / Software Development
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.