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- Tasks: Design and build data models to deliver reliable insights across BI platforms.
- Company: Join a global media group with diverse brands in publishing, audio, and advertising.
- Benefits: Enjoy hybrid work options and competitive salary between £80,000 - £90,000.
- Why this job: Shape the future of data strategy while collaborating with innovative teams.
- Qualifications: Strong SQL skills and experience with dimensional modelling in dbt required.
- Other info: Opportunity to engage with stakeholders and influence data product development.
The predicted salary is between 64000 - 76000 £ per year.
A global, privately-owned media group with brands spanning across publishing, audio, and out-of-home advertising, is seeking an Analytics Engineer to join their growing data team.
As the business matures its data capabilities, they are evolving toward a clearer separation between Data Engineering, Analytics Engineering, and Data Product disciplines. This role will sit firmly in the Analytics Engineering function, focused on modelling and building the semantic layer that powers consistent, reliable insights across the company’s BI and data science platforms.
This role will focus on the “middle layer", designing dimensional models, building reusable dbt pipelines, and ensuring the semantic layer meets the needs of Power BI, Looker, and data science teams. There’s also the opportunity to stretch into data product work, partnering with stakeholders to understand and translate business requirements into robust data models.
Key Responsibilities:- Build and maintain well-structured, scalable data models using dbt and Kimball/Medallion architecture.
- Develop and own the semantic layer, shifting calculations and business logic out of BI tools and into the model layer to drive consistency across Looker, Power BI, and other downstream consumers.
- Work closely with Data Engineers responsible for ingestion (from source systems to raw layers such as S3 or cloud storage), but focus your efforts on the modelling and transformation stage.
- Collaborate with the Data Product team to ensure the semantic layer serves evolving business and analytical needs.
- Support best practices in CI/CD (using GitHub) for productionising and maintaining dbt pipelines.
- Contribute to a common, reusable data model that serves BI, Data Science, and AI/ML teams alike.
- Strong experience with SQL and dimensional modelling in dbt.
- Proven experience building and maintaining semantic layers in modern data platforms.
- Familiarity with Medallion architecture, CI/CD processes (GitHub), and version-controlled data workflows.
- BI visualisation experience, such as Looker or Power BI preferred.
- Understanding of data ingestion and orchestration tools (e.g., Airflow, Terraform) is also beneficial.
- Exposure to streaming or log data handling would also be advantageous, though not required.
- Strong communication skills to engage with Data Product stakeholders and translate business needs into technical solutions.
If you’re an Analytics Engineer with experience in building semantic layers, dimensional modelling, and enabling self-serve BI and data science from a unified data model, this is an outstanding opportunity to shape the future data strategy of a major media brand.
Senior Analytics Engineer employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Analytics Engineer
✨Tip Number 1
Familiarise yourself with the specific tools and technologies mentioned in the job description, such as dbt, Power BI, and Looker. Having hands-on experience or projects showcasing your skills with these platforms can set you apart during discussions.
✨Tip Number 2
Network with professionals in the analytics engineering field, especially those who work with semantic layers and dimensional modelling. Engaging in relevant online communities or attending industry meetups can provide insights and connections that may help you during the application process.
✨Tip Number 3
Prepare to discuss your previous experiences in building and maintaining semantic layers. Be ready to share specific examples of how your work has driven consistency and improved data accessibility for BI and data science teams.
✨Tip Number 4
Showcase your understanding of CI/CD processes and version-controlled data workflows. Being able to articulate how you've implemented best practices in your past roles will demonstrate your readiness to contribute effectively to our data team.
We think you need these skills to ace Senior Analytics Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with SQL, dimensional modelling, and building semantic layers. Use specific examples from your past roles that demonstrate your skills in these areas.
Craft a Compelling Cover Letter: In your cover letter, explain why you are passionate about analytics engineering and how your background aligns with the responsibilities of the role. Mention your familiarity with tools like dbt, Looker, and Power BI to show you’re a great fit.
Showcase Relevant Projects: If you have worked on projects involving CI/CD processes or data modelling, include them in your application. Describe your role and the impact of your work on the overall project outcomes.
Highlight Communication Skills: Since the role requires collaboration with Data Product stakeholders, emphasise your communication skills. Provide examples of how you've successfully translated business needs into technical solutions in previous positions.
How to prepare for a job interview at Harnham
✨Showcase Your Technical Skills
Be prepared to discuss your experience with SQL and dimensional modelling in dbt. Highlight specific projects where you've built and maintained semantic layers, as this is crucial for the role.
✨Understand the Business Context
Research the company’s brands and their data needs. Be ready to explain how your technical skills can translate into business solutions, especially in relation to BI tools like Looker and Power BI.
✨Demonstrate Collaboration Skills
Since the role involves working closely with Data Engineers and the Data Product team, be prepared to share examples of how you've successfully collaborated in past projects. Emphasise your communication skills and ability to engage stakeholders.
✨Familiarise Yourself with CI/CD Practices
Brush up on your knowledge of CI/CD processes, particularly using GitHub. Be ready to discuss how you’ve implemented best practices in productionising and maintaining dbt pipelines in previous roles.
