At a Glance
- Tasks: Build scalable data models and develop automated data pipelines using modern tools.
- Company: Join a forward-thinking company focused on data innovation and sustainability.
- Benefits: Competitive salary, generous holiday, healthcare plan, and electric car scheme.
- Other info: Dynamic team environment with opportunities for growth and development.
- Why this job: Shape data standards and practices while working with cutting-edge cloud technologies.
- Qualifications: Strong SQL skills and experience with data modelling and transformation tools.
The predicted salary is between 45000 - 55000 £ per year.
We’re looking for an Analytics Engineer to join our Data & Analytics team, helping to build scalable, reliable and well‑governed data products using modern analytics engineering practices. This role is centred on data modelling, transformation and pipeline development, with a strong focus on dbt‑style workflows and cloud data platforms (e.g. Snowflake). You’ll play a key role in shaping how data is structured and delivered across the organisation, enabling high‑quality, self‑service analytics. While the role includes exposure to Power BI, the focus is on engineering robust datasets and semantic models that underpin reporting, rather than building dashboards.
What you’ll be doing:
- Build scalable data models and transformation layers
- Design and develop reusable, well‑structured data models to support analytics use cases
- Apply best practices in dimensional modelling and semantic layer design
- Build data transformation workflows using modern tools (e.g. SQL, dbt or equivalent)
- Develop and operate data pipelines
- Create and maintain automated ELT pipelines to transform and deliver data from multiple sources
- Ensure pipelines are reliable, efficient and scalable within a cloud data environment (e.g. Snowflake)
- Promote standardisation, modular design and reusability across data transformations
- Drive data quality and engineering best practice
- Implement testing frameworks (e.g. data validation, schema tests, lineage) to ensure data accuracy
- Maintain documentation, version control and structured development practices
- Contribute to the adoption of modern analytics engineering standards across the team
- Enable downstream analytics (Power BI)
- Develop and maintain datasets and semantic models for Power BI consumption
- Validate and test data within Power BI to ensure consistency with underlying models
- Support programmatic interaction with Power BI (e.g. metadata extraction, automation, integration with data workflows)
- Ensure strong alignment between upstream models and downstream reporting outputs
- Collaborate with analysts and stakeholders
- Translate business requirements into scalable, reusable data assets
- Enable self‑service analytics by delivering clean, well‑structured datasets
- Work closely with analysts to improve data usability and consistency
- Optimisation and continuous improvement
- Improve performance of queries, transformations and data models
- Identify opportunities to automate processes and enhance efficiency
- Contribute to tooling, frameworks and shared development patterns
Who we’re looking for:
- Strong experience with SQL and data modelling (e.g. dimensional modelling, semantic modelling)
- Experience with modern data transformation tooling (e.g. dbt, strongly preferred)
- Experience working with cloud data platforms (e.g. Snowflake, Azure, AWS)
- Experience building and maintaining scalable data pipelines and datasets
- Understanding of data quality, testing and governance practices
- Experience working with Power BI datasets/semantic models (rather than solely report development)
- Experience with Python or scripting for automation desirable
- Exposure to version control (e.g. Git) and collaborative development practices
Why is this role different?
- Focus on data modelling and transformation — not reporting delivery
- Built around modern analytics engineering (dbt, ELT, testing, version control)
- Strong emphasis on cloud data platforms (e.g. Snowflake)
- Power BI used as a consumer layer, not the primary skill set
- Opportunity to help shape standards, tooling and engineering practices
Benefits:
- The opportunity to participate in our annual, performance‑related bonus plan and valuable share schemes
- Generous pension contribution
- Life assurance
- Healthcare Plan (permanent employees only)
- At least 25 days holiday, plus public holidays, 26 days after 2 years’ service. There’s also the option to buy and sell holiday
- Competitive family leave
- Participate in our electric car scheme, which offers employees the option to hire a brand‑new electric car through tax efficient salary sacrifice (permanent employees only)
- There are many discounts we offer – both for our own products and at a range of high street stores and online
- We’re creating net‑zero carbon workplaces by 2030 by investing in our sustainable, modern offices across the UK, all designed to bring people together and elevate the in‑person experience
Data Analyst (Analytics Engineer) in Hove employer: Legal & General
Join a forward-thinking organisation that prioritises innovation and employee well-being, offering a collaborative work culture where your contributions as a Data Analyst will directly impact the development of scalable data products. With generous benefits including performance-related bonuses, a robust pension scheme, and a commitment to sustainability, you'll find ample opportunities for professional growth in a supportive environment that values your expertise in modern analytics engineering practices.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analyst (Analytics Engineer) in Hove
✨Tip Number 1
Network like a pro! Reach out to current employees on LinkedIn or attend industry meetups. It’s all about making connections that can help us get our foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data models and transformation projects. This gives us a chance to demonstrate our expertise beyond just a CV.
✨Tip Number 3
Prepare for interviews by brushing up on SQL and dbt. We should be ready to discuss our experience with cloud platforms like Snowflake and how we’ve tackled data quality issues.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure our application gets noticed and shows we’re genuinely interested in joining the team.
We think you need these skills to ace Data Analyst (Analytics Engineer) in Hove
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your SQL expertise, data modelling experience, and any work with cloud platforms like Snowflake. We want to see how you can contribute to our Data & Analytics team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about analytics engineering and how your background aligns with our needs. Don’t forget to mention your experience with dbt and data pipelines – we love that stuff!
Showcase Your Projects:If you've worked on relevant projects, whether in a professional or personal capacity, make sure to include them. Describe your role in building data models or pipelines, and how you ensured data quality. This gives us a glimpse of your hands-on experience!
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 important updates. We can’t wait to hear from you!
How to prepare for a job interview at Legal & General
✨Know Your Data Modelling
Make sure you brush up on your data modelling skills, especially dimensional and semantic modelling. Be ready to discuss how you've applied these practices in previous roles, as this will show your understanding of the core responsibilities of the Analytics Engineer position.
✨Familiarise with dbt and Cloud Platforms
Since the role heavily focuses on dbt and cloud data platforms like Snowflake, it’s crucial to demonstrate your experience with these tools. Prepare examples of how you've built scalable data pipelines or transformation workflows using dbt, and be ready to discuss any challenges you faced.
✨Showcase Your Collaboration Skills
This role involves working closely with analysts and stakeholders, so be prepared to share experiences where you translated business requirements into data assets. Highlight how you’ve enabled self-service analytics and improved data usability in past projects.
✨Emphasise Continuous Improvement
Talk about your approach to optimising queries and enhancing efficiency in data processes. Share specific instances where you identified opportunities for automation or contributed to tooling and frameworks, as this aligns with the company’s focus on continuous improvement.
