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- Tasks: Own and architect data infrastructure for impactful commercial tools.
- Company: Join a dynamic team at a global organisation focused on data-driven success.
- Benefits: Hybrid working, competitive salary, and opportunities for professional growth.
- Why this job: Make a real impact on business outcomes with your data skills.
- Qualifications: Strong SQL and Python skills, experience with dbt and GCP.
- Other info: Collaborative environment with mentorship opportunities and a focus on continuous learning.
The predicted salary is between 36000 - 60000 £ per year.
We are looking for an Analytics Engineer to join a high-impact data team building products that directly influence commercial performance and revenue growth. This role sits at the intersection of data engineering, analytics, and product, with clear visibility on how technical decisions translate into real business outcomes.
You will take ownership of the data infrastructure that powers revenue-generating tools used by sales and commercial teams. From designing scalable data pipelines to building robust data models, you will create the foundations that enable real-time insights, automated lead generation, and smarter decision-making across the organisation.
This is an opportunity to scale proven data products from successful prototypes into enterprise-grade platforms, while mentoring others and shaping best practice as the data estate grows.
What you’ll be doing:
- Owning and architecting end-to-end data infrastructure for commercial and sales-facing tools
- Designing and building scalable ELT pipelines and data models to support applications, dashboards, and analytics products
- Writing and optimising SQL and Python to process large, complex datasets
- Building and maintaining dbt models, tests, and documentation
- Monitoring pipeline health, data quality, and performance metrics
- Leading technical architecture discussions and making design decisions that support future scale
- Collaborating closely with analytics, data engineering, sales operations, and market intelligence teams
- Mentoring team members on analytics engineering best practices
- Ensuring high standards around testing, version control, CI/CD, and documentation
What you’ll need:
- Strong SQL skills for large-scale data transformations
- Strong Python skills for data pipeline development
- Hands-on experience with dbt / dbt Cloud
- Experience working in GCP, particularly BigQuery
- Infrastructure-as-code experience (e.g. Terraform)
- Strong experience with Git and modern version control workflows
- Solid understanding of data modelling (dimensional models, star)
- Experience implementing data quality and testing frameworks
What will help you succeed:
- Strong architectural thinking and ability to design for scale
- Proactive approach to identifying data quality and performance issues
- Ability to communicate clearly with non-technical stakeholders
- Experience mentoring or guiding other engineers
- Familiarity with CI/CD pipelines for data transformations
- Knowledge of enterprise data warehouse design principles
- Exposure to geospatial analytics (e.g. BigQuery GIS)
- Experience working with data visualisation tools such as Tableau
- Interest in advanced analytics, predictive modelling, or AI-driven insights
- Understanding of data governance, lineage, and metadata management
- Experience with modern data stack tools (e.g. Airbyte, Fivetran)
- A continuous-learning mindset in a fast-evolving data environment
Why join?
- Work on data products with direct, measurable commercial impact
- High ownership and influence in a small, collaborative team
- Mix of hands-on technical work and strategic architecture decisions
- Hybrid working with regular in-person collaboration in London
- Opportunity to shape how data is used across a growing, global organisation
Analytics Engineer employer: Holt Executive
Contact Detail:
Holt Executive Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Analytics Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. 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 data projects, especially those involving SQL, Python, and dbt. This gives you a chance to demonstrate your expertise and makes you stand out when chatting with hiring managers.
✨Tip Number 3
Prepare for interviews by brushing up on common analytics engineering questions. Be ready to discuss your experience with data pipelines, GCP, and how you've tackled data quality issues in the past. Practice makes perfect!
✨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, it shows you’re genuinely interested in joining our awesome team at StudySmarter.
We think you need these skills to ace Analytics Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Analytics Engineer. Highlight your SQL and Python skills, and any experience with dbt or GCP. We want to see how your past experiences align with what we’re looking for!
Showcase Your Projects: Include specific examples of projects where you’ve designed scalable data pipelines or built data models. We love seeing real-world applications of your skills, so don’t hold back on the details!
Be Clear and Concise: When writing your cover letter, keep it straightforward. Explain why you’re excited about the role and how you can contribute to our data team. We appreciate clarity and enthusiasm!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Holt Executive
✨Know Your Tech Stack
Make sure you’re well-versed in SQL and Python, as these are crucial for the role. Brush up on your dbt skills and be ready to discuss how you've used these tools in past projects. Being able to articulate your experience with GCP and BigQuery will also give you an edge.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled data quality issues or optimised data pipelines in previous roles. Think about specific challenges you faced and how your architectural thinking led to scalable solutions. This will demonstrate your proactive approach and technical expertise.
✨Communicate Clearly
Since you'll be collaborating with non-technical stakeholders, practice explaining complex concepts in simple terms. Be ready to discuss how you’ve successfully communicated technical decisions in the past, ensuring everyone is on the same page.
✨Emphasise Mentorship Experience
If you have experience mentoring others, highlight this during your interview. Discuss how you’ve guided team members on best practices in analytics engineering, as this aligns with the role's responsibilities and shows your leadership potential.
