Analytics Engineer
Indexed description
About The Role
We're looking for an Analytics Engineer to bridge the gap between data engineering and business analytics, enabling reliable, scalable, and well-structured data across the organization. In this role, you will design and build robust data models that power reporting, insights, and decision-making, while ensuring consistency, quality, and trust in our data ecosystem.
You'll work closely with analysts, stakeholders, and data engineers to translate business needs into clean, reusable data structures, supporting everything from BI dashboards to self-serve analytics. This is a great opportunity for someone who enjoys building strong data foundations, shaping metric definitions, and driving data maturity in a fast-growing SaaS environment.
Responsibilities
- Design and build core data models (Customer, Subscription, Revenue) in Snowflake using dbt, structuring Silver and Gold layers to support reliable reporting and analysis across the business
- Implement and maintain standardised metric logic (MRR, churn, CAC, retention) in collaboration with the Head of Data and stakeholders, ensuring consistent, reusable definitions across all tools
- Implement data quality checks, testing, monitoring and documentation in dbt, ensuring trust and transparency in all data outputs
- Partner with analysts and stakeholders to translate business questions into structured data models; support BI tools with curated data sources
- Implement and maintain data contracts and model standards in collaboration with the Data Engineer, reducing pipeline breakages and ensuring schema stability
- Continuously improve and evolve data models as business needs change; support self-serve analytics adoption
- Strong SQL skills and experience working with data warehouses (e.g. Snowflake)
- Hands-on experience with data transformation tools (e.g. dbt preferred)
- Experience working with layered data architectures (e.g. Bronze/Silver/Gold) and data modelling concepts (dimensional modelling, canonical models, semantic layers)
- Experience working with complex datasets from multiple sources (product, CRM, billing)
- Experience working with subscription or revenue metrics (e.g. MRR, churn, retention) is highly preferred
- Experience implementing data quality testing and validation
- Familiarity with version control (Git) and collaborative workflows
- Ability to translate business requirements into scalable data models
- Strong analytical and structured thinking with a business mindset
- Attention to detail and data quality
- Ability to work cross-functionally with both technical and non-technical stakeholders
- Pragmatic and solution-oriented approach
- Comfortable working in a fast-paced, evolving environment
- Have a passion for AI solutions and self-serve capabilities
- Competitive salary
- Company-wide bonus scheme and a great Stock Option plan
- Amazing workplace, certified as Great Place to Work
- Hybrid Work From Home policy
- Office gym, nutritionist, and meal vouchers
- Individual training budget for professional development
- Private medical insurance plan
- Fun and collaborative start-up environment (with amazing offices!)
- Flexible working arrangements
- Commuting Expenses
- Equal opportunity and workplace diversity
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