Location
London

Hours
Full Time

Salary
Competitive salary based on experience

About the Role
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 will 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.

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-promoted applications at scale. You will help deliver reliable, scalable, observable, and secure data solutions across cloud-native services, lakehouse architectures, data warehousing, and streaming systems. You will partner with teammates to build consistent, maintainable pipelines and contribute across the software delivery lifecycle from requirements through support.

Key responsibilities include building and maintaining scalable, reusable data processing and data quality frameworks using Python, PySpark, and dbt; operating batch and streaming data pipelines with strong scalability, performance, and fault tolerance; developing and managing workflow orchestration using tools such as Apache Airflow; implementing and optimizing data models and warehouse structures to support analytics and business intelligence workloads; writing clean, testable Python/PySpark code using object-oriented principles and unit testing; implementing infrastructure-as-code for the data platform using Terraform; containerizing and deploying services using Docker, Kubernetes, and Helm; contributing across the software development lifecycle; collaborating in an agile, dynamic environment; and applying enterprise-authorized AI capabilities to accelerate data pipeline design and validation while ensuring data sensitivity and security compliance.

Experience
- At least 5 years of recent, hands-on professional experience actively coding as a data engineer
- Experience working in an agile and dynamic environment
- Experience across the software development lifecycle including requirements, design, architecture, development, testing, deployment, release, and support
- Hands-on experience with major cloud technologies such as AWS, Google Cloud, or Azure
- Proficiency in Python using object-oriented programming and unit/integration testing practices
- Experience with SQL and SQL-based workflow management tools such as dbt
- Experience with orchestration tools such as Apache Airflow or similar
- Understanding of messaging/streaming systems such as Kafka or Pub/Sub
- Familiarity with infrastructure-as-code tools like 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 before use, escalating when uncertain and following data handling requirements

About you
Detail-oriented and collaborative with a strong commitment to engineering excellence, operational reliability, and security. Comfortable working in a fast-paced, agile environment and partnering with cross-functional teams to deliver reliable data solutions. Strong problem-solving skills and a proactive approach to continuous improvement and innovation.

Qualifications
- Degree in Computer Science or a STEM-related field (or equivalent)
- Preferred skills include data modeling, experience with data streaming and scalable processing frameworks (e.g., Spark, Flink, Beam), automation of deployment and testing in CI/CD pipelines, familiarity with lakehouse patterns and table formats such as Apache Iceberg, experience with federated query engines like Trino, designing automated tests including mocking frameworks, and container-based deployment environments such as Docker and Kubernetes.