At a Glance
- Tasks: Design and build data pipelines for analytics and business decision-making.
- Company: Tech-driven company with a focus on collaborative engineering.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic role with ownership and exposure to cutting-edge engineering practices.
- Why this job: Make a real impact by solving complex data challenges with modern tools.
- Qualifications: 3-6 years in data engineering, strong Python and SQL skills required.
The predicted salary is between 50000 - 60000 £ per year.
Our client, a technology-led business with a growing engineering presence, is looking to hire a Data Engineer into a collaborative and delivery-focused team. This role sits at the centre of building the pipelines, platforms and data foundations that support analytics, modelling and wider business decision-making. It's a strong opportunity for someone who enjoys owning technical problems end-to-end, working with modern data tooling, and partnering with a range of stakeholders to turn complex data needs into reliable, scalable solutions.
What you'll be doing:
- Design and build data pipelines that bring together information from multiple internal and external sources.
- Develop and improve scalable data products that support analytics and evolving business needs.
- Help ensure data is reliable, well-structured and accessible for downstream use.
- Work closely with engineers, analysts and stakeholders to solve practical data challenges.
- Contribute to testing, troubleshooting and the ongoing improvement of data platforms.
What they're looking for:
- Around 3-6 years' experience in data engineering or a related role.
- Strong hands-on skills in Python and SQL.
- Experience building data-intensive systems and working with large-scale datasets.
- Exposure to modern data tooling such as Airflow, Spark, Databricks, or similar platforms.
- Familiarity with at least one major cloud environment such as AWS, Azure or GCP.
- A strong communicator who enjoys working in a collaborative, delivery-oriented environment.
- A degree in Computer Science or another quantitative discipline would be beneficial.
Why consider it:
- A chance to work on meaningful data infrastructure with clear impact across the wider business.
- Broad exposure to modern engineering practices, varied technical problems and strong stakeholder interaction.
- A role with genuine ownership for someone who wants to combine hands-on delivery with practical business value.
Data Engineer employer: Selby Jennings
Join a forward-thinking technology-led business that values collaboration and innovation, offering Data Engineers the chance to work on impactful data infrastructure. With a strong emphasis on employee growth, you will have access to modern engineering practices and the opportunity to tackle diverse technical challenges while working closely with stakeholders. This role not only provides ownership of your projects but also fosters a supportive work culture that prioritises meaningful contributions to the business.
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 Selby Jennings!
✨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 Selby Jennings.
✨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 Selby Jennings.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineer at Selby Jennings, 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 Selby Jennings, 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 Selby Jennings. 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 Selby Jennings
✨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 Selby Jennings!
✨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.
