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).

• 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 improve stability and delivery, and use advanced developer tooling to accelerate delivery while operating within firm standards and control requirements
• Need to have Modern tooling expectations for this role include: Python programming for data engineering, orchestration, automation, and developer productivity, GitHub Copilot for assisted development, subject to firm approval, policy, and applicable control requirements and Claude Code for assisted development, subject to firm approval, policy, and applicable control requirements

Required Qualifications, Capabilities, and Skills

• 5+ years of relevant experience in data engineering, analytics engineering, or data platform engineering roles, with demonstrated delivery across the data lifecycle from collection through transformation, modeling, and analytics enablement
• Strong proficiency in SQL, hands-on programming experience in Python, and experience with data query paradigms including SQL and NoSQL;
• Practical experience with data modeling, data integration/ETL processes, and interoperability across multiple business systems, including data migration and mapping complex relational data between systems
• Experience with database technologies such as PostgreSQL, MySQL, and MongoDB, including performance optimization and operational management
• Familiar with big data and analytics engines/platforms such as Apache Spark and Hadoop, and with open-source analytics/query engines for big data
• Experience implementing, or partnering closely on, data quality, metadata, and governance controls that increase reliability and auditability
• Understand modern distributed systems patterns including APIs and distributed event streaming, and can operate effectively in cloud-based and event-driven environments
• Demonstrate strong analytical and problem-solving skills, attention to detail, and the ability to work independently and collaboratively in a matrix environment, with effective communication skills to build partnerships across business and technology stakeholders

Preferred Qualifications, Capabilities, and Skills

• Familiarity with GraphQL is a plus
• A degree (or equivalent practical experience) in Computer Science, Information Systems, Data Science, or a related field is preferred (per provided job specifications). Experience with scenario generation and modeling approaches that support resiliency and recovery risk analysis is preferred, particularly where outputs must be explainable and operationally actionable for control stakeholders (per provided job specifications, supplemented with role intent).
• Exposure to statistical and analytical techniques and data science methods, including familiarity with data mining techniques, is preferred (per provided job specifications). Experience producing high-quality data architecture artifacts such as target-state diagrams, data flows and lineage views, and conceptual/logical models consumable by a broad stakeholder group is also preferred (per provided job specifications). Industry accreditation such as TOGAF or cloud/solution architecture certifications is a plus