Data Analyst

Analysis & insights

• Conduct deep-dive analyses to identify trends, opportunities, and risks across product, customer, and business data.
• Support strategic decisions by providing actionable insights to product, engineering, commercial, and operations teams.
• Partner with stakeholders to define metrics, KPIs, and reporting needs.

Reporting and visualization

• Build and maintain dashboards and reports in Power BI (and potential other BI tools).
• Create clear and compelling data visualizations that make insights accessible to non-technical stakeholders.
• Ensure consistent definitions, documentation, and data integrity across reporting.

Experimentation and measurement

• Assist in designing and analyzing A/B tests and experiments to evaluate product and business initiatives.
• Track and measure the impact of AI-powered features and internal productivity tools.

Data accessibility and enablement

• Enable self-service analytics by developing standardized datasets, dashboards, and documentation.
• Promote data literacy across teams through training and knowledge-sharing.

Collaboration and support

• Proven ability to work within a structured request system, prioritize competing requests, clearly communicate timelines, and collaborate with stakeholders to clarify the objective behind each data request.
• Work closely with engineering to ensure reliable, clean, and well-structured data pipelines.
• Support the Data Science & Analytics Manager in delivering cross-company data initiatives.

Experience levels

• 2-4 years in data/analytics, with exposure to data engineering or ML workflows
• Strong proficiency in SQL and Excel
• Has worked in a small or scaling company
• Has influenced senior stakeholders using data
• Evidence of owning projects end-to-end rather than only executing tasks

Skillset (including transferables)

• SQL at an advanced level, comfortable writing transformations and optimising queries
• Experience with a modern analytics stack (DBT, cloud warehouse, BI tool)
• Ability to translate ambiguous business questions into structured analysis
• Strong written communication in a remote-first team
• Basic data engineering principles (pipelines, version control, testing)
• Commercial awareness - understands metrics like LTV, CAC, churn
• Stakeholder management and ability to influence without authority
• Experimentation mindset - understands A/B testing, causal thinking, and KPI development.
• Clear storytelling with data
• Transferable skills: problem structuring, prioritisation, systems thinking, ability to learn new tools quickly

Professional memberships

• Desirable, but not required:
• Published articles, blogs, talks, or teaching
• Mentorship involvement (mentor or mentee)

Qualifications & additional certifications

• Strong quantitative background (STEM degree or equivalent practical experience)

Desirable, but not required:

• Prior experience in SaaS, EdTech, or B2C/B2B2C environments
• Experience working with modern data platforms (Snowflake, BigQuery, Databricks)
• Experience with Python
• Interest in AI and how analytics can support emerging technologies
• Certification in a cloud platform (Azure, AWS, GCP)
• Certification in Power BI, Tableau, or similar
• Data Build Tool fundamentals or equivalent

• 30 days' holiday per year (inclusive of UK bank holidays)
• 100% remote & flexible working #LI-Remote
• Social events and annual meet ups