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- Tasks: Shape and deliver a modern data strategy while building scalable data pipelines.
- Company: Join an 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 opportunities for ownership and leadership.
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 employer: Datatech Analytics
Contact Detail:
Datatech Analytics Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer | Remote
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend virtual meetups, and connect with fellow data enthusiasts. 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 projects, especially those involving ELT/ETL pipelines or data modelling. 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 cloud platforms and analytics tools. Practising common interview questions can help you feel more confident when it’s your turn to shine.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for passionate data engineers like you. Plus, applying directly can sometimes give you a better chance of getting noticed by hiring managers.
We think you need these skills to ace Data Engineer | Remote
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 data stacks.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're excited about this role at StudySmarter. Share your passion for data and how you can contribute to our refreshed data strategy and analytics platform.
Showcase Your Technical Skills: Don’t forget to mention your strong SQL skills and any experience with Python or similar languages. If you've worked with tools like dbt or Airflow, make sure to include that too – we love seeing relevant technical expertise!
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’re considered for this exciting opportunity to join our team.
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, Azure, or GCP. 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 questions into robust data assets, which is key for the role.
✨Understand the Business Impact
Research the company’s mission and values, and think about how your work as a Data Engineer can contribute to their goals. Be ready to explain how you can help drive better insights and smarter decisions using data. This shows that you’re not just technically skilled, but also aligned with their vision.
✨Ask Thoughtful Questions
Prepare some insightful questions to ask during the interview. Inquire about their current data strategy, the tools they use, or how they measure success in data projects. This not only shows your interest in the role but also helps you gauge if the company is the right fit for you.
