Data Analyst (Product Analytics)

LondonPermanentmid

Data AnalystData

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Quick Summary

Overview

We’re the Moonpig Group – home to Moonpig, Greetz, Red Letter Days and Buyagift – and we’re on a mission to make people feel loved, celebrated and remembered. Whether it’s a card that gets them laughing out loud or a gift that makes their day, we help people stay close, no matter the miles.

Technical Tools

bigquerygoogle-analyticsmixpanelpandaspythonsnowflakesqltableauab-testingdata-analysisecommerce

We’re proud to be leading the online gifting revolution, with brilliant products, clever tech and a whole lot of heart. Our platform makes it easy to create moments that matter – packed with personal touches and delivered with care.

We’re not just about selling cards or gifts – we’re here to spread joy, spark smiles and make every celebration feel extra special. And with values that guide how we work and support one another, we’ve built a place where people (and ideas) can truly thrive.

If you’re looking to make an impact, bring your spark and be part of something meaningful – we’d love to have you on the team. 🌙🐷

About the Role

Join Moonpig Group as a Data Analyst (Product Analytics) and help shape better product decisions through data. Working closely with Product, Design, and Engineering, you’ll explore customer behaviour, support experimentation, and uncover the “why” behind performance.

How will you do this? You’ll explore how customers interact with our platform and help teams design experiments to test the impact of the changes we make on customer behaviour.

This is a great opportunity to combine analytical thinking with real product impact—translating data into clear, actionable recommendations that improve how our products perform and how customers experience them. You’ll also continue to grow your technical toolkit, developing skills in Python, modelling, and causal inference over time.

• Support product decision-making by analysing performance, identifying patterns in user behaviour, and contributing to data-informed recommendations
• Contribute to experimentation by helping design, run, and analyse A/B tests, ensuring results are robust and clearly communicated
• Explore and explain drivers of customer behaviour, including conversion and retention, using appropriate analytical techniques
• Support evaluation of AI, recommendation, and personalisation features alongside Data Science and Engineering teams
• Apply analytical and statistical methods using SQL and, where appropriate, Python to explore data and test hypotheses
• Contribute to scalable analytics practices by improving documentation, queries, and reusable analysis
• Conduct behavioural analysis (funnels, cohorts, retention, LTV) to support product insights and recommendations
• Apply causal thinking (with guidance), using experimental and quasi-experimental approaches where appropriate
• Use modern analytical workflows, including AI tools, to improve productivity while maintaining critical thinking

Experience in product analytics, data analysis, or a similar role (typically 2–4 years)
• Familiarity with ecommerce or tools such as Google Analytics is a must have
• Strong SQL skills, including joins, aggregations, and window functions
• Practical experience using Python for analysis (e.g. pandas, notebooks) – nice to have
• Exposure to experimentation, including supporting or running A/B tests
• Ability to structure problems and draw meaningful insights from data
• Clear communication skills, with the ability to explain findings and support recommendations
• Comfortable collaborating with product, design, and engineering stakeholders
• Exposure to personalisation, recommendations, or dynamic pricing is a bonus
• Experience with tools such as Tableau, Mixpanel, Git, Snowflake, or BigQuery is advantageous
• Awareness of causal inference or statistical methods is helpful but not essential