At a Glance
- Tasks: Build and maintain scalable data platforms and pipelines for analytics and reporting.
- Company: Join J.P. Morgan, a global leader in financial services with a focus on innovation.
- Benefits: Competitive salary, diverse work environment, and opportunities for professional growth.
- Other info: Collaborative, agile team environment with a commitment to diversity and inclusion.
- Why this job: Make a real impact by powering data-driven insights for over 275,000 investors.
- Qualifications: Degree in Computer Science or STEM, plus 5 years of hands-on data engineering experience.
The predicted salary is between 60000 - 80000 £ per year.
hackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this 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'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.
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.
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.
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).
J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs.
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.
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We think this is how you could land Data Engineer III in Bristol
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We think you need these skills to ace Data Engineer III in Bristol
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
