Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Build and optimise data solutions for reporting and insights in a fast-paced retail environment.
- Company: Join a rapidly growing retail business where data drives decisions and enhances customer experience.
- Benefits: Enjoy hybrid working, flexible hours, and opportunities for professional growth.
- Why this job: Make a real impact by solving data challenges and collaborating with diverse teams.
- Qualifications: Experience in data engineering or analytics, with skills in SQL, Python, and BI tools required.
- Other info: Ideal for problem solvers who thrive in dynamic environments and want to shape data strategies.
The predicted salary is between 36000 - 60000 £ per year.
Reports To: Head of Data
Location: Gillingham, Hybrid Working 3 days per week in office.
About the Role
Our retail client is a fast-growing business where data plays a critical role in driving decisions, improving customer experience, and enabling operational efficiency. As a Data Analytics Engineer, you'll be responsible for building and optimising scalable data solutions that power reporting, insights, and analytics across the organisation. This is a high-impact role ideal for someone who enjoys solving problems with data and collaborating across technical and business teams.
Key Responsibilities:
- Design, build, and manage robust data pipelines to extract, transform, and load data from various sources.
- Develop scalable, well-structured data models that support analytics and reporting needs.
- Create and maintain dashboards and reports using business intelligence tools.
- Implement data quality checks, monitoring, and validation processes.
- Collaborate with cross-functional teams to gather requirements and deliver actionable insights.
What You'll Need:
- Proven experience in data engineering, analytics engineering, or a similar role.
- Proficiency in SQL and/or Python for data transformation and automation.
- Hands-on experience with cloud data warehouses (e.g. Snowflake, Redshift, BigQuery).
- Strong understanding of data modelling, ETL/ELT workflows, and data validation.
- Experience with BI tools such as Power BI, Tableau, or Looker.
- Ability to translate complex data into clear insights for non-technical stakeholders.
What Will Make You Stand Out:
- Experience with modern data stack tools such as dbt, Fivetran, and Snowflake.
- Familiarity with Azure cloud services and automation frameworks.
- Understanding of data challenges in large-scale retail environments.
- Clear, confident communicator who can simplify technical findings for business users.
- Strong stakeholder management skills and a track record of delivering value from data.
If you are interested in this role please apply, to follow up please email me.
Data Analytics Engineer employer: LinkedIn
Contact Detail:
LinkedIn Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Analytics Engineer
✨Tip Number 1
Familiarise yourself with the specific data tools mentioned in the job description, such as SQL, Python, and cloud data warehouses like Snowflake or Redshift. Having hands-on experience with these technologies will not only boost your confidence but also demonstrate your readiness for the role.
✨Tip Number 2
Showcase your problem-solving skills by preparing examples of how you've used data to drive decisions in previous roles. Be ready to discuss specific projects where you built data pipelines or created dashboards that had a measurable impact on business outcomes.
✨Tip Number 3
Network with professionals in the data analytics field, especially those who work in retail. Engaging with industry peers can provide insights into the challenges they face and help you understand what skills are most valued in this sector.
✨Tip Number 4
Prepare to articulate how you can simplify complex data findings for non-technical stakeholders. Practising this skill will not only help you in interviews but also show that you can bridge the gap between technical and business teams effectively.
We think you need these skills to ace Data Analytics Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data engineering and analytics. Emphasise your proficiency in SQL, Python, and any cloud data warehouse tools you've used, such as Snowflake or Redshift.
Craft a Compelling Cover Letter: In your cover letter, explain why you're passionate about data analytics and how your skills align with the role. Mention specific projects where you've built data pipelines or created dashboards, showcasing your problem-solving abilities.
Showcase Technical Skills: Include a section in your application that lists your technical skills, particularly those mentioned in the job description, like ETL/ELT workflows and BI tools. This will help demonstrate your fit for the role at a glance.
Prepare for Potential Questions: Think about how you would explain complex data concepts to non-technical stakeholders. Be ready to provide examples of how you've collaborated with cross-functional teams to deliver actionable insights.
How to prepare for a job interview at LinkedIn
✨Showcase Your Technical Skills
Be prepared to discuss your experience with SQL, Python, and cloud data warehouses. Bring examples of past projects where you built data pipelines or created dashboards, as this will demonstrate your hands-on expertise.
✨Understand the Business Context
Research the retail client and their data needs. Be ready to explain how your work can improve customer experience and operational efficiency, showing that you understand the impact of data on business decisions.
✨Communicate Clearly
Practice explaining complex data concepts in simple terms. Since you'll be working with non-technical stakeholders, being able to translate technical findings into actionable insights is crucial.
✨Prepare for Collaboration Questions
Expect questions about teamwork and collaboration. Think of examples where you've worked with cross-functional teams to gather requirements or deliver insights, highlighting your stakeholder management skills.
