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Greater London, England, United Kingdom
Hybrid
At Moonpig Group, data powers how we create personalised experiences, make better decisions, and drive growth. As a Lead Analytics Engineer, you'll play a key role in shaping how data is modelled, governed, and consumed across the business.
Operating at a business domain level, you'll combine deep technical expertise with commercial thinking to define, prioritise, and deliver analytics engineering solutions that enable scalable, trusted, and actionable data. You'll work closely with stakeholders across Product, Marketing, Finance, Engineering, Data Science, and the wider Data team, helping turn complex challenges into high-impact data products.
As a technical leader within Analytics Engineering, you'll help set standards, mentor others, and influence the future direction of our data platform. This is an opportunity to make a meaningful impact on how Moonpig uses data to innovate, personalise customer experiences, and scale effectively.
• Lead the planning and delivery of analytics engineering initiatives across business domains, aligning work with strategic priorities.
• Own the delivery of scalable data models and datasets, coordinating contributions from other engineers where required.
• Partner with stakeholders across Product, Marketing, Finance, Data Science, and Engineering to define requirements and shape solutions.
• Challenge and influence stakeholders to drive scalable, sustainable, and high-impact data solutions.
• Act as a technical leader for analytics engineering, promoting best practices in data modelling, testing, documentation, and governance.
• Design and implement scalable, reusable data models using dbt and Snowflake.
• Lead architectural decisions, balancing performance, cost, scalability, and usability.
• Contribute to the evolution of analytics engineering standards, tooling, and data platform capabilities.
• Make and own technical decisions, balancing trade-offs between speed, scalability, and cost.
• Ensure high standards of data quality through testing, monitoring, and governance practices.
• Use metrics such as data quality, pipeline performance, and adoption to drive continuous improvement.
• Identify opportunities to optimise processes, tooling, and workflows.
• Translate complex business and data challenges into scalable data models and actionable delivery plans.
• Advocate for best practices in data modelling, governance, and data usage across the organisation.
• Represent Analytics Engineering in cross-functional discussions, helping teams navigate priorities and trade-offs.
• Mentor and support analytics engineers through code reviews, pairing, and knowledge sharing.
• Contribute to raising the overall quality, consistency, and maturity of the analytics engineering function.
• Support the optimisation and scalability of the Snowflake data platform, ensuring performance, security, and cost efficiency.
• Evaluate and introduce new tools and technologies that improve platform capability and engineering effectiveness.
• Drive adoption of standardised, well-documented data models across the business.
Extensive experience in analytics engineering, data modelling, or related data disciplines, with a proven track record of operating across complex business domains.
• Advanced SQL expertise, including complex data modelling, transformation, and performance optimisation.
• Strong experience designing and maintaining scalable data models using dbt.
• Proven experience working with Snowflake and modern cloud-based data platforms.
• Deep understanding of analytics architecture, data governance, and scalable data design principles.
• Experience writing clean, efficient Python code for automation and data processing.
• Strong knowledge of Git and collaborative software development practices.
• Ability to influence stakeholders and build strong cross-functional relationships.
• Experience operating with autonomy and making decisions in ambiguous environments.
• Passion for mentoring, coaching, and developing other engineers.
• Comfortable working within agile delivery environments.
• Curiosity for emerging technologies, tools, and best practices within the data ecosystem.
• Experience with DataDog, Dagster, Fivetran, or Tableau would be beneficial but is not essential.
• Data Stack: Snowflake, dbt, SQL, Python, Fivetran, Dagster, DataDog, Tableau.
• Infrastructure: AWS (SageMaker, EC2, Lambda, Glue, S3), Terraform, API Gateway.
• Collaboration Tools: GitHub, Jira, Confluence.
• Analytics Tools: GA4, GTM, GCP BigQuery.
We don't expect you to have experience with every technology listed above. We're interested in people who are excited to learn, collaborate, and help us continue evolving our data capabilities.
• Recruiter Screening Call
• Hiring Manager Interview
• Technical Test
• Final Interview with Wider Team Members
Our process may vary depending on role and availability. We keep candidates informed of any changes.
