Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Transform raw data into actionable insights and build scalable data pipelines.
- Company: Join a fast-growing tech unicorn redefining how we connect and work.
- Benefits: Competitive salary up to £75k, flexible working, and a creative culture.
- Other info: Dynamic team with opportunities for growth and innovation.
- Why this job: Make a real impact with data-driven decisions in a collaborative environment.
- Qualifications: 3+ years in data roles, expert SQL skills, and experience with dbt.
The predicted salary is between 54000 - 84000 £ per year.
About the company
Join a fast-growing, global tech unicorn that is redefining the way people connect and work in real life. This company is on a mission to simplify complex processes and empower teams with data-driven insights. With a collaborative, in-person-first culture, you’ll be part of a forward-thinking organisation that values creativity, autonomy, and innovation.
The role
We are looking for an Analytics Engineer to turn raw data into clean, reliable, and actionable datasets and visualisations for business users. You will sit at the intersection of Data Engineering and Data Analytics, helping to build scalable, high-quality data pipelines and models that support decision-making across the business.
What you’ll do
- Design, build, and maintain scalable ELT pipelines using tools like dbt to transform raw data into structured, analysis-ready models.
- Implement robust data modelling and software engineering best practices to ensure accuracy, performance, and usability.
- Develop data quality tests, monitoring, and documentation to maintain trust in business data.
- Collaborate with analysts, engineers, and business stakeholders to optimise data structures for reporting and analytical workflows.
- Optimise queries and models to improve performance of the data warehouse and downstream applications.
- Build compelling data visualisations to empower teams across the organisation.
- Advocate for modern data stack tools and engineering best practices, including version control and CI/CD.
What we’re looking for
- 3+ years in a data-focused role such as Analytics Engineer, Data Analyst, or BI Developer.
- Expert-level SQL skills and experience writing complex queries.
- Hands-on experience with dbt or similar transformation frameworks.
- Experience with cloud data warehouses like Snowflake, BigQuery, or Redshift.
- Strong understanding of ETL/ELT principles, dimensional modelling, and data warehousing concepts.
- Familiarity with BI tools such as Looker and version control systems like Git.
Nice-to-haves
- Python experience for automation and orchestration.
- Knowledge of semantic layers and orchestration tools such as Airflow.
- Familiarity with modern software engineering practices applied to data, including testing, modularity, and code review.
Analytics Engineer employer: Xcede
Contact Detail:
Xcede Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Analytics Engineer
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or even just grab a coffee with someone who works in analytics. Building relationships can lead to job opportunities that aren’t even advertised.
✨Show Off Your Skills
Create a portfolio showcasing your best work. Whether it’s data visualisations, models, or projects you’ve completed, having tangible examples of your skills can really impress potential employers. Don’t forget to share it on platforms like LinkedIn!
✨Ace the Interview
Prepare for interviews by practising common questions and scenarios related to analytics engineering. Be ready to discuss your experience with SQL, dbt, and cloud data warehouses. Show them how you can turn raw data into actionable insights!
✨Apply Through Our Website
When you find a role that excites you, apply directly through our website. It shows your enthusiasm and gives us a chance to see your application first-hand. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Analytics Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Analytics Engineer role. Highlight your SQL expertise, experience with dbt, and any cloud data warehouse knowledge to catch our eye!
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about data and how you can contribute to our mission. Share specific examples of your past work that align with the responsibilities listed in the job description.
Showcase Your Projects: If you've worked on relevant projects, whether in a professional setting or as personal endeavours, include them! We love seeing how you've applied your skills in real-world scenarios, especially with data visualisations and pipelines.
Apply Through Our Website: We encourage you to apply directly through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any updates from us!
How to prepare for a job interview at Xcede
✨Know Your Data Tools
Make sure you’re well-versed in the tools mentioned in the job description, like dbt and SQL. Brush up on your skills and be ready to discuss how you've used these tools in past projects. This shows you’re not just familiar with them, but you can also apply them effectively.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in data engineering or analytics roles. Think of examples where you optimised data pipelines or improved data quality. This will demonstrate your ability to tackle real-world problems and contribute to the company's mission.
✨Collaborate Like a Pro
Since the role involves working with various stakeholders, be ready to talk about your experience collaborating with analysts and engineers. Share examples of how you’ve worked together to optimise data structures or create impactful visualisations. This highlights your teamwork skills and adaptability.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s data practices and culture. Inquire about their modern data stack or how they advocate for best practices. This shows your genuine interest in the role and helps you assess if the company is the right fit for you.