Senior Data Engineer III

As a Data Engineer JPMorgan Chase within Personal Investing , you will build and operate a robust cloud-native data platform and pipelines that power analytics, regulatory reporting, and data-driven applications at scale. You will help us deliver reliable, scalable, observable, and secure data solutions across cloud-native services, lakehouse architectures, data warehousing, and streaming systems. You’ll partner with teammates to build consistent, maintainable pipelines and contribute across the software delivery lifecycle from requirements through support.

Build and maintain scalable, reusable data processing and data quality frameworks using Python, PySpark, and dbt Build and operate batch and streaming data pipelines with strong scalability, performance, and fault tolerance Develop and manage workflow orchestration using tools such as Apache Airflow to support reliable, observable, and well-scheduled data movement and transformations Implement and optimize data models and warehouse structures to support analytics and business intelligence workloads Write clean, testable Python/PySpark code using object-oriented principles and unit testing Implement infrastructure-as-code for the data platform using Terraform Containerize and deploy services using Docker, Kubernetes, and Helm Contribute across the software development lifecycle, including requirements, design, development, testing, deployment, release, and support Collaborate with teammates in an agile, dynamic environment to deliver reliable outcomes Required qualifications, capabilities, and skills: Degree in Computer Science or a STEM-related field (or equivalent practical experience) Agile environment experience working in dynamic settings Software development lifecycle expertise (requirements, design, architecture, development, testing, deployment, release, and support) 5+ years hands-on coding as a data engineer Cloud technologies proficiency (e.g., AWS, Google Cloud, or Azure) Python programming (object-oriented principles, unit/integration testing) SQL skills and familiarity with SQL-based workflow tools (e.g., dbt) Orchestration tools experience (e.g., Airflow or similar) Messaging/streaming systems understanding (e.g., Kafka or Pub/Sub) Infrastructure-as-code familiarity (e.g., Terraform) for cloud-based data infrastructure Enterprise-authorized AI capabilities usage to support data engineering workflows, including validation habits , data sensitivity awareness , and ability to review and validate AI-assisted outputs before use, escalating when uncertain and following data handling requirements Preferred qualifications, capabilities, and skills Data modeling skills Experience with data streaming and scalable processing frameworks (e.g., Spark, Flink, Beam, or similar) Experience automating deployment, releases, and testing in continuous integration and continuous delivery pipelines Experience with lakehouse patterns and table formats (e.g., Apache Iceberg) Experience with federated query engines such as Trino Experience designing automated tests (unit, component, integration, and end-to-end), including use of mocking frameworks Experience with containers and container-based deployment environments (e.g., Docker, Kubernetes, or similar)