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For Companies At a Glance
- 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 Slough employer: Datatech Analytics
Contact Detail:
Datatech Analytics Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer - Remote in Slough
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or join relevant online communities. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Prepare for those interviews! Research the company’s data strategy and think about how your skills can contribute. Practise common data engineering questions and be ready to showcase your SQL and Python prowess.
✨Tip Number 3
Show off your projects! If you've built any cool data pipelines or analytics tools, make sure to highlight them. A portfolio can really set you apart and demonstrate your hands-on experience.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Data Engineer - Remote in Slough
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 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 similar languages. If you've worked with tools like dbt or Airflow, make sure to include that too – we love seeing those details!
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 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 ELT/ETL processes and tools like Snowflake or Databricks will definitely impress.
✨Showcase Your Projects
Prepare to share specific examples of projects you've worked on that relate to data engineering. Highlight how you’ve built scalable pipelines or optimised data models. This not only shows your technical skills but also demonstrates your ability to turn questions into robust data assets, which is key for this role.
✨Understand Stakeholder Needs
Since this role involves partnering with analysts and non-technical stakeholders, be ready to discuss how you’ve communicated complex data concepts to different audiences. Think of examples where you’ve turned their questions into actionable insights, showcasing your ability to bridge the gap between technical and non-technical teams.
✨Emphasise Your Growth Mindset
This company values personal growth and development, so express your eagerness to learn and take on more responsibility. Share instances where you’ve taken initiative in your previous roles, whether it’s improving data quality or contributing to best practices. Show them you’re not just looking for a job, but a place to grow your career.
