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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 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 Manchester employer: Datatech Analytics
Contact Detail:
Datatech Analytics Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer | Remote in Manchester
✨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 cloud data warehousing. 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 data modelling and cloud platforms. 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 in Manchester
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 your technical prowess!
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 brush up on your knowledge of modern data stacks, especially tools like Snowflake and Databricks. Be ready to discuss how you've used these technologies in your previous roles, as well as any challenges you've faced and how you overcame them.
✨Showcase Your SQL Skills
Since strong SQL skills are a must for this role, prepare to demonstrate your proficiency. You might be asked to solve a problem or optimise a query during the interview, so practice common SQL scenarios beforehand to show off your expertise.
✨Communicate with Stakeholders
This role involves partnering with analysts and non-technical stakeholders, so be prepared to discuss how you've effectively communicated complex data concepts in the past. Share examples of how you've turned questions into actionable insights, highlighting your ability to bridge the gap between technical and non-technical teams.
✨Emphasise Your Growth Mindset
The company values personal growth and development, so express your eagerness to learn and take on new challenges. Share specific instances where you've stepped up in your career, showcasing your confidence and curiosity in the data engineering field.
