Lead Data Engineer

Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated.

Together we will create a brighter future and make a meaningful difference.

As a Lead Data Engineer at JPMorganChase within the Commercial & Investment Bank Operational Resiliency team, you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way.

As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm's business objectives.

You will design and build resilient, well-governed data products and pipelines that enable end-to-end lineage, high-quality analytics, and scenario generation to model technology resiliency and recovery risk (per provided job specifications).

You will partner closely with cybersecurity, technology controls, engineers, and business stakeholders to deliver pragmatic solutions aligned to strategic goals, with a strong bias toward production-grade engineering discipline and measurable operational outcomes (per provided job specifications, supplemented with hiring manager requirements).

Job Responsibilities


* Design, build, and operate production-grade data pipelines that ingest, clean, transform, and aggregate data from disparate sources to deliver trusted data products


* Evolve logical and physical data models that create a comprehensive view of user flows, system dependencies, resiliency signals, and risk measures, and develop new models that support prediction and decisioning where appropriate


* Translate business, risk, and control requirements into implementable technical designs and a pragmatic delivery plan, partnering with architects, data engineers, analysts, and stakeholders across a matrix organization.

You will contribute to the broader data architecture strategy that underpins resiliency analytics and risk modeling, including integration and interoperability across data sources and systems


* Implement and continuously improve data quality management, metadata management, and data governance practices to increase reliability, explainability, and auditability, and enable data lineage and traceability across sources, transformations, and curated outputs


* Work with modern architectures and patterns (including microservices, event-driven designs, cloud-based data platforms, and Lambda/Kappa patterns) to support scalable and, where needed, near real-time data requirements (per provided job specifications).


* Leverage SQL heavily and apply a strong understanding of NoSQL and other database technologies, managing and optimizing databases for performance and efficiency


* Follow embed automation and engineering best practices (version control, CI/CD, code review, testing, and documentation) to i...