Data Analyst

Job Description

Huel’s growth across e-commerce, retail, and new markets is generating more data than ever and we’re scaling our Data & Analytics team to keep decisions sharp and fast. As our next Data Analyst, you’ll turn behavioural and commercial data into clear, actionable insights for Marketing, Trading, Product and Retail. From owning deep-dive analyses to building measurement frameworks and self-serve dashboards, you’ll help ensure day-to-day and strategic decisions are insight-led and impactful.

Below is a list of the types of things you will be involved in:

• Supporting end-to-end analysis– Help translate questions from Marketing, Trading, Product, and Retail into simple analytical tasks, run basic analysis, and present clear findings.

• Assisting with data experiments– Support the design and evaluation of basic A/B tests and other studies, helping to communicate the results simply.
• Helping define how we measure success – Assist the team in setting up basic Key Performance Indicators (KPIs) and tracking plans for new projects, making sure we have a clear way to measure success.
• Cleaning and preparing data– Work with the Data Engineering team to ensure data sets are accurate, help document data, and support basic checks to keep our data smooth.
• Maintaining self-serve analytics – Help develop and update live dashboards, keep data documentation current, and assist with training sessions so other teams can easily find routine answers.
• Identifying new opportunities– Use basic data skills to help the team size up new products, markets, and campaigns, focusing on potential impact.

Not yet sure if this is the right role for you? Click HERE to read a more detailed job description.

What we’re looking for in you

This role is suited to someone with 1–3 years’ experience in data or analytics who’s ready to take the next step in their career. You’re commercially curious, eager to learn fast, and motivated by turning analysis into real business impact.

To succeed, you’ll bring experience in some of the following areas and a willingness to quickly develop in the rest:

• SQL– you can write simple queries to explore data and are keen to learn more about optimising them.
• Product analytics – familiar with platforms like Heap, Amplitude, or Mixpanel and ready to learn how to set up events and track user journeys.
• Marketing analytics– strong understanding of attribution modelling and the role of different channels in the buying process
• Statistical– a basic understanding of AB testing, econometrics and other statistical analysis methods.
• Measurement mindset – an understanding of why tracking plans and KPIs are important.
• Data modelling– a willingness to learn about data modelling and how to keep data accurate.
• Storytelling– you can summarise your analysis into a clear narrative that makes sense to people in Marketing, Engineering, and Leadership.
• AI/Machine Learning – a foundational understanding of how machine learning models work and an interest in applying AI tools to automate reporting or uncover new insights.