Analytics Engineer

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 Big Query
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. Big Query 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