At a Glance
- Tasks: Build and maintain scalable data pipelines for impactful analytics.
- Company: Join a dynamic team in a supportive, inclusive environment.
- Benefits: Enjoy 26 days holiday, wellness allowance, and hybrid working options.
- Other info: Great opportunities for personal growth and team collaboration.
- Why this job: Make a real difference with your skills in a fast-paced tech landscape.
- Qualifications: 3-7 years in data engineering, strong Python and SQL skills required.
The predicted salary is between 50000 - 70000 £ per year.
The Data Engineer is responsible for building and maintaining scalable, reliable, and high-quality data pipelines and infrastructure that power data products and analytics across the organisation. The role focuses on ensuring data is accurate, timely, and consistently processed, particularly in environments involving high-volume transactional and API-driven systems.
Key Responsibilities
- Data Pipeline Development
- Design, build, and maintain ETL/ELT pipelines
- Ingest data from internal systems and external APIs
- Develop scalable data processing workflows (batch and/or real-time)
- Ensure pipelines are reusable, efficient, and maintainable
- Data Integration
- Integrate data from various sources, including transactional flows
- Handle complex data scenarios such as:
- Event sequencing (e.g. bet → resolve)
- Idempotency and duplicate handling
- Partial or delayed data
- Ensure consistency between source systems and analytical datasets
- Data Transformation & Modelling Support
- Implement transformation logic aligned with architectural data models
- Build and maintain structured data layers for analytics consumption
- Collaborate closely with the Data Architect on model implementation
- Data Quality, Monitoring & Reliability
- Implement data validation, monitoring, and alerting mechanisms
- Identify and resolve data inconsistencies or failures
- Ensure high levels of data accuracy and availability
- Performance & Scalability
- Optimise pipelines for performance and cost-efficiency
- Support scaling of data infrastructure as volumes grow
- Ensure low-latency data availability where required
- Collaboration
- Work closely with Data Architect, Analysts, and Manager
- Support Analysts by ensuring availability of curated datasets
- Contribute to continuous improvement of data platform capabilities
Required Skills & Experience
- 3–7+ years experience in data engineering, backend engineering, or similar roles
- Strong programming skills (e.g. Python, SQL)
- Solid understanding of ETL/ELT processes and data pipeline design
- Experience working with APIs and integrating distributed systems
- Experience handling transactional or event-based data
- Strong understanding of:
- Data transformation techniques
- Data warehousing concepts
- Data modelling fundamentals
- Experience with data orchestration and workflow tools
- Ability to build robust, fault-tolerant systems
- Strong problem-solving skills with attention to detail and data accuracy
Nice to Have
- Experience in iGaming, fintech, or high-volume transactional environments
- Experience with event streaming technologies (e.g. Kafka)
- Familiarity with modern data stack tools (e.g. Snowflake, BigQuery, dbt, Airflow)
- Experience with real-time or near real-time data processing
- Understanding of idempotent processing and event ordering
- Exposure to CI/CD and infrastructure-as-code practices
- Experience supporting analytics or BI teams
Success Metrics
- Reliability and uptime of data pipelines
- Data freshness and latency
- Reduction in data errors and inconsistencies
- Performance and scalability of data processing
- Availability of high-quality, curated datasets for analysts
What’s in it for you?
- Experience a dynamic and team-orientated work environment.
- Opportunities for personal growth and learning
- An open, inclusive and supportive team where you will be valued, and your suggestions will be welcome.
- 26 days paid holiday per year. This is in addition to local bank holidays.
- Competitive salary
- €400 annual wellness Allowance
- Hybrid Working
- Risk Benefits such as pension, Life Assurance (4x annual salary), Private Medical Insurance
- Team Building Opportunities
- Flexible core hours between 10am – 4pm
- Receive support whenever you need it with our Employee Assistance Program, available 24/7.
- Local discounts and more.
Data Engineer employer: Eeze
Eeze is an excellent employer that prioritises the well-being and professional growth of its employees. With a supportive work environment, generous benefits like 26 days of paid holiday, and opportunities to collaborate with talented engineering and IT teams, you will find meaningful and rewarding employment while enhancing your skills in security engineering.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Eeze!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Engineer at Eeze.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Eeze.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineer at Eeze, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Data Engineer
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!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Eeze, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Eeze. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Eeze
✨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!
✨Showcase Your Projects
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
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Eeze!
✨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.
