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Data Analyst

A data analyst gathers, cleans and examines data to answer questions, turning raw figures into clear findings that help an organisation decide what to do next.

Part of the occupations cluster in the employment knowledge graph — connected to occupations, documents, hiring guides, career guides, country guides and employment law.

What a data analyst does

A data analyst helps an organisation understand what its data is saying. The role covers collecting and cleaning data, running analysis, building reports and dashboards, and explaining findings in plain language.

It blends technical work with communication: querying data sources, checking quality, spotting patterns and presenting results so that non-specialists can act on them. The tools and data vary by sector and team.

Core responsibilities

  • Collecting data from relevant sources.
  • Cleaning and preparing data for analysis.
  • Querying and analysing data to answer questions.
  • Building reports, dashboards and visualisations.
  • Explaining findings clearly to stakeholders.
  • Checking data quality and flagging issues.
  • Supporting decisions with evidence and context.

Skills and qualities

  • Analytical thinking and curiosity.
  • Comfort with querying tools and spreadsheets.
  • Attention to detail and data-quality awareness.
  • Clear communication and visualisation of findings.
  • Basic statistical understanding appropriate to the role.
  • Problem-solving and structured working.
  • Discretion when handling sensitive data.

Typical employers and settings

  • Technology, retail and services companies.
  • In-house analytics and data teams.
  • Financial services and consultancies.
  • Public sector and research organisations.
  • Start-ups and scale-ups building reporting.

How the role is recruited

  • Roles are advertised directly or filled through recruiters.
  • Screening reviews a CV, analytical experience and tooling, plus right to work.
  • Interviews explore querying, analysis and how findings are communicated.
  • Some processes include a practical data or SQL exercise.
  • Onboarding covers data sources, tools, governance and stakeholders.

Documents involved

  • A job description outlining the data and tools used.
  • A CV and any portfolio of analysis or dashboards.
  • An employment contract including data-handling terms.
  • An offer letter confirming role, level and start date.
  • Right-to-work and identity checks as required.
  • An onboarding checklist for data access and tools.

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For informational purposes only. This is neutral, educational guidance — not legal, employment-law, immigration, payroll, tax, financial or compliance advice, and not an interpretation of any law. It contains no salary or compensation data, no benchmarks or averages, no fabricated studies, surveys or case studies, and no software, vendor or provider rankings. Requirements vary by jurisdiction, industry and contract and change over time. Confirm all specifics with qualified professionals before acting.
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FAQ

Frequently asked questions

What does a data analyst do?

They gather, clean and examine data to answer questions, building reports and dashboards and explaining findings so the organisation can decide what to do next.

What skills does a data analyst need?

Analytical thinking, comfort with querying tools and spreadsheets, attention to detail, clear communication of findings and a basic statistical understanding suited to the role.

What is the difference between a data analyst and a business analyst?

A data analyst focuses on examining data to answer questions, while a business analyst focuses on understanding needs and shaping requirements. The roles can overlap depending on the team.

Do data analysts need a degree?

Not always. Some employers prefer a numerate or computing degree, while others focus on practical skills and a portfolio; requirements depend on the specific job.

How are data analysts usually hired?

Through direct adverts or recruiters, with a CV review and an interview covering querying and analysis, often a practical data exercise, then onboarding to data sources and tools.