Data Engineer III

Build the data foundation behind a digital investing experience used by over 275,000 investors in the UK. Join Personal Investing to help deliver clear, data-driven insights through robust cloud-native platforms and pipelines. You’ll work with modern lakehouse, warehousing, and streaming technologies while strengthening engineering excellence and operational reliability. This is an opportunity to grow your impact on a platform that supports analytics and regulatory reporting at scale.

Job summary

As a Data Engineer at 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-promoten 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.

Job responsibilities

• 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
• Uses enterprise-authorized AI capabilities within the work environment to accelerate data pipeline/design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements
• Applies reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines (e.g., test generation and control validation), ensuring traceability/auditability and alignment to resiliency and security expectations

Required qualifications, capabilities, and skills

• Degree in Computer Science or a STEM-related field (or equivalent)
• Experience working in an agile and dynamic environment
• Experience across the software development lifecycle (requirements, design, architecture, development, testing, deployment, release, and support)
• At least 5 years of recent, hands-on professional experience actively coding as a data engineer
• Hands-on experience with major cloud technologies (e.g., AWS, Google Cloud, or Azure)
• Experience writing Python using object-oriented programming and unit/integration testing practices
• Experience with SQL and familiarity with SQL-based workflow management tools such as dbt
• Experience with orchestration tools such as Airflow (or similar)
• Understanding of messaging/streaming systems such as Kafka or Pub/Sub (or similar)

• Familiarity with infrastructure-as-code (e.g., Terraform) for cloud-based data infrastructure

  
  
• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity

• Ability to review and validate AI-assisted outputs (e.g., query suggestions, test ideas, or model change summaries) 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)