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- Tasks: Design and optimise data models for actionable insights in sports analytics.
- Company: Leading sports pricing provider with a focus on innovation and collaboration.
- Benefits: Competitive salary, substantial bonuses, flexible hybrid/remote work options.
- Other info: Dynamic environment with opportunities for growth and collaboration with top analysts.
- Why this job: Join a pioneering team and make an impact in the exciting world of sports analytics.
- Qualifications: Proficient in SQL and Python, with experience in data modelling and cloud platforms.
The predicted salary is between 40000 - 55000 £ per year.
We are a proprietary sports pricing and product provider, specializing in the development of intricate, simulation-driven pricing and risk systems that empower leading sports brands. As pioneers in player-level, play-by-play simulations and forecasting, we deliver the group’s most advanced pricing and risk capabilities – particularly focused on the US market.
The purpose of the data engineer is to design, build, and optimize data models and analytics workflows that enable accurate, timely, and actionable insights across the business. This role bridges the gap between data engineering and analytics, ensuring that data is transformed into well-structured, reliable datasets for reporting, visualization, and advanced analytics. The role holder will focus on creating scalable, maintainable solutions that empower stakeholders to make data-driven decisions efficiently.
Key responsibilities:- Design and implement data models optimized for analytics and reporting use cases.
- Develop and maintain ELT/ETL pipelines to transform raw data into curated datasets.
- Collaborate with analysts and data scientists to understand requirements and deliver high-quality data products.
- Optimize SQL queries and workflows for performance and scalability.
- Ensure data quality, consistency, and governance across all analytics layers.
- Implement best practices for documentation, testing, and reproducibility in analytics workflows.
- Work with cloud-based tools and services (e.g., AWS S3, Athena, ECS, CloudFormation, Lambdas, CloudWatch) to support analytics infrastructure.
- Contribute to the development of dashboards and self-service analytics tools.
- Competent in SQL and Python for data transformation and analytics.
- Understanding of data modelling concepts (e.g., star schema, dimensional modelling).
- Experience (1+ year) working with relational databases and designing optimized schemas.
- Ability to debug and optimise slow queries and inefficient workflows.
- Familiarity with cloud-based data platforms and services (AWS preferred).
- Excellent communication skills for collaborating with technical and non-technical stakeholders.
- Strong problem-solving and analytical mindset.
- Experience with BI tools (e.g., Power BI, Plotly/Dash) for visualisation.
- Experience structuring APIs – Django, Flask, FastAPI.
- Familiarity with distributed systems (e.g., Spark, Kafka) for large-scale analytics.
- Knowledge of testing practices (e.g., TDD) in data workflows.
- Passion for clean, well-documented systems and reproducibility.
Seniority level: Associate
Employment type: Full-time
Job function: Information Technology
Junior Data Engineer in England employer: OTA Recruitment
Contact Detail:
OTA Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Junior Data Engineer in England
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data models, analytics workflows, or any projects you've worked on. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your SQL and Python skills. Be ready to discuss your experience with data transformation and analytics, and don’t forget to highlight your problem-solving abilities!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are genuinely interested in joining our team and contributing to our innovative projects.
We think you need these skills to ace Junior Data Engineer in England
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Junior Data Engineer role. Highlight your SQL and Python skills, and any experience with data modelling or cloud services. We want to see how your background fits with what we do!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about sports analytics and how you can contribute to our team. Keep it concise but engaging – we love a good story!
Showcase Your Projects: If you've worked on any relevant projects, make sure to mention them! Whether it's a personal project or something from your previous job, we want to see your hands-on experience with data transformation and analytics.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, we love seeing applications come in through our own channels!
How to prepare for a job interview at OTA Recruitment
✨Know Your Data Models
Make sure you brush up on data modelling concepts like star schema and dimensional modelling. Be ready to discuss how you've applied these in your previous work or projects, as this will show your understanding of the role's requirements.
✨Show Off Your SQL Skills
Prepare to demonstrate your SQL prowess. You might be asked to optimise a slow query or explain how you would structure a database for analytics. Practising common SQL problems can help you feel more confident during the interview.
✨Familiarise Yourself with Cloud Tools
Since the company uses AWS services, it’s a good idea to get comfortable with tools like S3, Athena, and Lambda. Being able to discuss how you’ve used these tools in past projects will definitely give you an edge.
✨Communicate Clearly
You’ll need to collaborate with both technical and non-technical stakeholders, so practice explaining complex concepts in simple terms. This will showcase your communication skills and your ability to bridge the gap between data engineering and analytics.