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JP Morgan Chase

Runs a consumer bank and an investment bank

Lead Data Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeNew York, NY, USA$147.3k - $190kIn-person

About the Job

Requirements

  • At least 5 years of relevant experience in data engineering, analytics engineering, or data platform engineering, with demonstrated delivery across the data lifecycle from collection through transformation, modeling, and analytics enablement.
  • Strong proficiency in SQL, hands-on Python programming experience, and experience with SQL and NoSQL data query paradigms.
  • Practical experience with data modeling, data integration and extract, transform, load (ETL) processes, and interoperability across multiple business systems, including data migration and mapping complex relational data between systems.
  • Experience with PostgreSQL, MySQL, and MongoDB, including performance optimization and operational management.
  • Familiarity with Apache Spark, Hadoop, and open-source analytics or query engines for big data.
  • Experience implementing, or closely partnering on, data quality, metadata, and governance controls that increase reliability and auditability.
  • Understanding of modern distributed systems patterns, including application programming interfaces (APIs) and distributed event streaming, and ability to operate in cloud-based and event-driven environments.
  • Ability to work independently and collaboratively in a matrix environment, with attention to detail and effective communication to build partnerships across business and technology stakeholders.

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 models that support prediction and decision-making 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.
  • Contribute to the broader data architecture strategy underpinning 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 microservices, event-driven designs, cloud-based data platforms, and Lambda/Kappa patterns to support scalable and, where needed, near real-time data requirements.
  • Use SQL extensively and apply understanding of NoSQL and other database technologies, managing and optimizing databases for performance and efficiency.
  • Apply automation and engineering best practices, including version control, continuous integration and continuous delivery (CI/CD), code review, testing, and documentation, to improve stability and delivery while operating within firm standards and control requirements.
  • Use Python for data engineering, orchestration, automation, and developer productivity; use GitHub Copilot and Claude Code for assisted development subject to firm approval, policy, and applicable control requirements.

Desired Qualifications

  • Familiarity with GraphQL.
  • A degree or equivalent practical experience in Computer Science, Information Systems, Data Science, or a related field.
  • Experience with scenario generation and modeling approaches supporting resiliency and recovery risk analysis, particularly where outputs must be explainable and operationally actionable for control stakeholders.
  • Exposure to statistical and analytical techniques and data science methods, including data mining techniques.
  • Experience producing data architecture artifacts such as target-state diagrams, data flows and lineage views, and conceptual or logical models for broad stakeholder use.
  • Industry accreditation such as TOGAF or cloud or solution architecture certifications.

About the Company

JP Morgan Chase

Runs a consumer bank and an investment bank

JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.

Company size
10,001+
Company stage
IPO
Headquarters
New York City, New York
Founded
1799

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