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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-timeColumbus, OH, USANo salary listedIn-person

About the Job

Requirements

  • Formal training or certification in software engineering concepts and at least 5 years of applied experience.
  • At least 10 years of software engineering experience, with strong depth in data engineering or big data platforms.
  • Strong hands-on development experience in Java and proficiency in at least one data-focused language such as Python.
  • Experience designing and building robust ETL/ELT pipelines and data integration frameworks.
  • Strong experience with Apache Spark and distributed processing concepts, including fault tolerance, partitioning, and performance tuning.
  • Strong understanding of data storage serialization and formats such as Parquet and Avro.
  • Solid knowledge of data lake/lakehouse concepts and patterns, including Medallion architecture, data quality, metadata, and governance considerations.
  • Experience with cloud data warehouses such as Snowflake, including loading/unloading patterns and performance basics, or equivalent.
  • Ability to lead technical discussions, communicate clearly to varied stakeholders, and drive delivery in cross-functional environments.
  • Demonstrated experience leading effective use of approved AI-assisted software development tools for coding, code review, test acceleration, and troubleshooting, and setting team expectations for validating AI outputs for correctness, performance, and security.
  • Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.

Responsibilities

  • Execute creative software solutions through design, development, and technical troubleshooting, using approaches beyond routine methods to build solutions or break down technical problems.
  • Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, including code review/refactoring, test strategy acceleration, and incident/root-cause analysis support; establish validation standards for secure coding, peer review, and automated testing; promote reuse of effective patterns.
  • Apply software development lifecycle toolchain knowledge, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
  • Lead architecture and hands-on delivery of large-scale data pipelines (ETL/ELT) for ingestion, transformation, validation, reconciliation, and publishing across curated layers.
  • Design and operationalize data lake patterns, including partitioning strategies, data quality controls, lineage, governance, and reusable datasets/data products.
  • Build and optimize distributed processing workloads using Apache Spark and modern storage/file formats such as Parquet and Avro.
  • Drive performance tuning across compute and storage, including Spark tuning for shuffle, joins, caching, and skew handling, and warehouse tuning where applicable.
  • Implement engineering best practices for code quality, automated testing, continuous integration and delivery, observability through metrics/logs/traces, security-by-design, and operational readiness.
  • Build and maintain Java-based services and components supporting data workflows, such as ingestion services, orchestration helpers, APIs, and data access layers.
  • Develop REST APIs and integration components for downstream consumption and platform interoperability.
  • Apply modern application engineering practices, including clean architecture, SOLID principles, test automation, and secure coding practices.

Desired Qualifications

  • Experience with orchestration and workflow scheduling tools such as Airflow, Dagster, or Control-M.
  • Exposure to streaming/event-driven processing such as Kafka or Spark Structured Streaming, and incremental processing patterns such as CDC and upserts.
  • Experience with table formats or ACID layers such as Delta Lake, Apache Iceberg, or Hudi.
  • A strong production operations mindset, including on-call practices, SLAs/SLOs, observability, and incident response.
  • Experience in regulated environments involving risk, audit, compliance, privacy, or PII handling.
  • Experience with cost optimization or FinOps practices for data platforms.
  • Experience working with Agentic AI/Gen AI solutions and capabilities.

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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