We’re 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’ll 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’ll 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 schemas)
- 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

TT