Analytics Engineer, Moonpig

• Strong SQL skills – you know how to write complex, clean queries
• Solid experience with dbt for building robust transformation pipelines
• Comfortable working in Snowflake, or similar cloud data platforms
• Some knowledge of Python for data tasks and automation
• Enjoy working in an agile, collaborative environment
• Technically curious – always keen to learn new tools and improve systems

Desirable

• As an Analytics Engineer, you’ll play a key role in turning raw data into reliable, well-modelled datasets that power insights, experimentation, and smart decision-making.
• Sitting in our Data Platform Team, you’ll be bridging the gap between data engineering and analytics, partnering with teams across Product, Marketing, and Engineering to make sure the right data is accessible, accurate, and actionable
• You’ll shape how we build and maintain transformation pipelines, support self-serve analytics, and help scale our data infrastructure so we can keep delivering meaningful customer experiences at pace
• Build scalable and reusable data models using dbt and Snowflake
• Own and optimise transformation pipelines, ensuring performance and cost-efficiency
• Develop data quality checks and monitoring using tools like Metaplane
• Collaborate with BI, Data Science, and Engineering teams to define requirements and deliver impactful datasets
• Document data models and promote best practices in analytics engineering
• Explore and integrate new tools (like Dagster) to improve scalability and workflow orchestration

• Stage 1: Recruiter screening call (20–30 mins)
• Stage 2: Interview with Hiring Manager and 1 other team member (45–60 mins)
• Stage 3: Final interview – split into two parts:
• 60 mins live test
• 30 mins culture/team fit session
• Offer! 🎉
• Please note: Our interview process may vary depending on the specific role, hiring team availability, or other business needs. We aim to keep candidates informed of any changes throughout the process