Junior/Mid/Senior Data Engineer - Hybrid, London
Location: London (hybrid: 3 days in office in Victoria)Contract Type: Full TimeRole Level: Junior/Mid/SeniorSalary: Junior - £38,000 - £55,000; Mid-Level - £55,000 - £75,000; Senior - £95,000 - £130,000 Reporting Line: Head of Data EngineeringDirect Reports: TBCThe Role
Prospect is on a mission to revolutionise decision making in sport. Our analytical solutions support decision making from the field through to the board room, helping our performance clients win trophies in four different sports, and winning awards for our commercial clients too. We have four primary lines of business: Performance Analytics, Sporting Product Analytics, Marketing Analytics & Execution, and Consulting & Advisory. Since closing our seed fundraising round in June 2021, Prospect has worked with rights holders, clubs, investors, broadcasters, and other service providers to the sports industry.We are looking for someone who is equally passionate about sports and analytics and is excited about the possibilities of the intersection of the two. The ideal candidate would have experience as a problem solver, data engineer, and communicator, preferably with a strong grasp of cloud computing concepts, preferably using AWS. You’ll work as part of cross-functional teams to help solve challenges and aid decision makers across the sporting landscape, from elite professional teams, to leagues and roadcasters, applying advanced analytics and modelling techniques.Personal Attributes
- Highly motivated and results-driven with a passion for technology.
- A passion for the business of sports, media and technology.
- Can-do attitude; a self-starter who wants to drive the company forward.
- Collaborative, open to feedback and willing to learn.
Junior Data Engineer
Responsibilities- Support development of scalable data pipelines.
- Assist with testing, validation and documentation of data assets.
- Learn from and collaborate with senior engineers.
- 2+ years relevant work experience.
- Foundational experience with Python and SQL.
- Eagerness to work with tools like dbt, Spark, DataBricks and Kedro.
- Understanding of version control, testing, and documentation.
- Familiarity with Agile, Jira, and Confluence.
- Proficiency working with AWS cloud services is required.
Mid-Level Data Engineer
Responsibilities- Build and maintain reliable data pipelines.
- Manage and optimise cloud storage and warehousing.
- Ensure data quality, security and maintainability.
- 4+ years relevant work experience.
- Strong SQL and Python skills.
- Experience with Snowflake, dbt, Spark.
- Version control, CI/CD and testing practices.
- Familiarity with Agile, Jira, and Confluence.
- Proficiency working with AWS cloud services is required.
Senior Data Engineer
Responsibilities- Architect robust data infrastructure and workflows.
- Lead technical design and platform evolution.
- Mentor engineers and drive operational excellence.
- 6+ years relevant work experience.
- Expertise in distributed data architecture and orchestration.
- Deep knowledge of Snowflake, S3, Athena, dbt.
- Security, performance, and governance leadership.
- Familiarity with Agile, Jira, and Confluence.
- Proficiency working with AWS cloud services is required.
How to apply?
- To apply, send your CV and covering letter to with the role that you are applying for in the subject line.
- You must have the right to work in the United Kingdom.
- Please state current notice period in the email.
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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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How to prepare for a job interview at Prospect
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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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.
