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- Tasks: Shape and deliver a modern data strategy while building scalable data pipelines.
- Company: Ambitious, values-led organisation focused on using data for good.
- Benefits: Competitive salary, fully remote work, and opportunities for technical growth.
- Why this job: Make a genuine impact by turning data into actionable insights.
- Qualifications: 2+ years in data engineering with strong SQL and cloud experience.
- Other info: Supportive environment with real runway for personal and professional development.
The predicted salary is between 36000 - 60000 £ per year.
A high visibility opportunity to join an ambitious, values led organisation as it refreshes its data strategy and modernises its intelligence platform. You’ll be trusted early, work closely with stakeholders, and help build the foundations that drive better insight, smarter decisions, and genuine impact, using data for good.
This role is well suited to someone early in their data engineering journey, around 2+ years’ experience, who’s ready to step up. You’ll join a supportive, encouraging environment with real runway to grow technically, while gradually developing ownership and leadership as your influence across the business increases.
What you’ll be doing:
- Helping shape and deliver a refreshed data strategy and modern analytics platform
- Building reliable, scalable ELT/ETL pipelines into a cloud data warehouse, Snowflake, Databricks, or similar
- Designing and optimising core data models that are dimensional, analytics-ready, and built to last
- Creating trusted data products that enable self-service analytics across the organisation
- Improving data quality, monitoring, performance, and cost efficiency
- Partnering with analysts, BI teams, and non-technical stakeholders to turn questions into robust data assets
- Contributing to engineering standards, best practice, and reusable frameworks
- Supporting responsible AI tooling, including programmatic LLM workflows where appropriate
What you’ll bring:
- 2+ years’ experience in data engineering within a modern data stack
- Strong SQL with a solid foundation in data modelling
- Python preferred, or similar, for pipeline development and automation
- Cloud experience across AWS, Azure, or GCP
- Familiarity with orchestration and analytics engineering tools such as dbt, Airflow, or equivalents
- Good habits around governance, security, documentation, version control (Git), and CI/CD
The kind of person who thrives here:
Confident, curious, and motivated. You care about doing things properly, enjoy being trusted and visible in the business, and are genuinely interested in using data to create positive outcomes.
Fully remote. No sponsorship, Post-Graduate Visa not supported.
Interested? Apply now.
Data Engineer - Remote in Luton employer: Datatech Analytics
Contact Detail:
Datatech Analytics Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer - Remote in Luton
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend virtual meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data engineering projects, especially those involving ELT/ETL pipelines or cloud data warehouses. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your SQL and Python skills. Be ready to discuss your past projects and how you've tackled challenges in data modelling or pipeline development. Practice makes perfect!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for passionate individuals who want to make a difference with data. Your next big opportunity could be just a click away!
We think you need these skills to ace Data Engineer - Remote in Luton
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experience mentioned in the job description. Highlight your 2+ years in data engineering and any relevant projects you've worked on, especially those involving cloud technologies like AWS or Azure.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're excited about this role and how you can contribute to our data strategy. Share specific examples of how you've built reliable ELT/ETL pipelines or improved data quality in previous roles.
Showcase Your Technical Skills: Don’t forget to mention your strong SQL skills and any experience with Python or orchestration tools like dbt or Airflow. We want to see how you’ve used these tools to create impactful data products.
Apply Through Our Website: We encourage you to apply directly through our website for a smoother application process. It’s the best way for us to receive your application and get to know you better!
How to prepare for a job interview at Datatech Analytics
✨Know Your Data Stack
Make sure you’re well-versed in the modern data stack mentioned in the job description. Brush up on your SQL skills and be ready to discuss your experience with cloud platforms like AWS or Azure. Being able to talk confidently about your past projects will show that you’re not just familiar with the tools, but you know how to use them effectively.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in data engineering and how you overcame them. Think of examples where you built reliable ELT/ETL pipelines or optimised data models. This will demonstrate your ability to turn complex problems into actionable solutions, which is key for this role.
✨Engage with Stakeholders
Since the role involves partnering with analysts and non-technical stakeholders, practice explaining technical concepts in simple terms. Be ready to share experiences where you’ve successfully collaborated with others to create data products or improve data quality. This shows you can bridge the gap between tech and business needs.
✨Emphasise Your Growth Mindset
The company values personal growth and development, so highlight your eagerness to learn and take on new challenges. Share examples of how you’ve sought feedback or pursued additional training in data engineering. This will resonate well with their supportive environment and show that you’re ready to step up.
