Senior Data Analyst

Role overview

The Senior Data Analyst will lead analysis within a product, project or business area, ensuring data is prepared, managed, interpreted and communicated effectively. The role supports decision-making through robust analysis, stakeholder engagement and clear visual storytelling.

You will work with multidisciplinary teams across data, AI, software engineering, product, QA and delivery to create practical outcomes for clients and end users.

Key responsibilities

• Lead the development of valuable statistical insights, analytical reports, dashboards and data products.
• Identify, prepare and link data from relevant sources, ensuring it is fit for purpose and reusable where possible.
• Apply appropriate analytical and statistical techniques, and support others in using them effectively.
• Quality assure analytical outputs, data models and reporting logic across the team.
• Communicate insight to technical and non-technical stakeholders, adapting the message to their needs.
• Support data governance, privacy and ethical considerations within the project or service area.

Essential skills and experience

• Strong data analysis experience, including data preparation, modelling, visualisation and reporting.
• Working knowledge of SQL and at least one BI or visualisation platform such as Power BI, Tableau or Looker.
• Ability to apply statistical and analytical techniques to answer business or user questions.
• Experience quality assuring analytical work and explaining limitations or assumptions.
• Strong communication skills with the ability to manage stakeholder expectations.
• Awareness of data governance, data ethics, privacy and secure handling of data.

Desirable skills and experience

• Experience with Python, R, dbt, cloud data platforms or ETL/ELT workflows.
• Experience leading small analytical workstreams or mentoring junior analysts.
• Experience working in Agile product, digital transformation or client delivery teams.

What success looks like

• Data is prepared, managed and used effectively to answer priority questions.
• Stakeholders understand the story behind the data and use it to make decisions.
• Analytical outputs are reliable, accessible and aligned to business goals.