Data Analyst Job Description
Hire a pragmatic Data Analyst who turns raw data into clear, actionable insight. This template helps you describe the role, required skills and everyday responsibilities so you attract candidates who can deliver reliable analyses and dashboards.
What does a Data Analyst professional do?
Look for someone with strong SQL and spreadsheet skills, experience building reports or dashboards, and the ability to explain findings to non-technical stakeholders.
Data Analyst job description template
Use this employer-ready example as a starting point, then replace the placeholders and adjust the content so it accurately reflects your vacancy.
Data Analyst
About the role
[Company name] is seeking a Data Analyst to join our [team/department]. You will extract, clean and analyse datasets, prepare dashboards and reports, and present insights to business stakeholders to support operational and strategic decisions. This role requires strong SQL skills, attention to data quality and the ability to communicate technical findings clearly. Location: [City / Remote / Hybrid]. Job type: [Full-time / Part-time / Contract].
Key responsibilities
- Write and maintain SQL queries to extract data from one or more databases or data warehouses.
- Clean, transform and validate datasets to ensure accuracy and suitability for analysis.
- Build and maintain dashboards and regular reports using a business intelligence tool or reporting platform.
- Perform ad-hoc analyses to answer business questions and quantify impact of initiatives.
- Define, track and report on key metrics; recommend measurable improvements.
- Collaborate with product, operations, finance or marketing teams to understand requirements and translate them into data specifications.
- Document data sources, transformations and analysis methods; keep analyses reproducible.
- Investigate data quality issues and work with engineering or data teams to resolve root causes.
Essential skills and experience
- Proven experience working as a Data Analyst or in a similar role.
- Strong SQL skills for querying relational databases.
- Fluent with spreadsheet tools (advanced formulas, pivot tables) and experience producing clear tables and charts.
- Ability to translate business questions into analytical approaches and to explain results to non-technical audiences.
- Attention to data quality and experience validating and cleaning datasets.
- Good organisational skills and experience documenting work so analyses are reproducible.
Desirable
- Experience with a scripting language for analysis such as Python or R.
- Familiarity with BI tools such as Tableau, Power BI, Looker or similar.
- Experience working with cloud data platforms or data warehouses (e.g. Snowflake, BigQuery, Redshift).
- Knowledge of basic statistics, A/B testing or causal inference techniques.
- Experience with data modelling, ETL processes or working alongside data engineering teams.
- Previous experience in the relevant industry (finance, e-commerce, health, etc.).
What we offer
- [Pension contribution scheme]
- [Generous annual leave allowance]
- [Flexible / hybrid working options]
- [Professional development budget or training allowance]
- [Private medical insurance / healthcare plan]
- [Cycle to work scheme or season ticket loan]
How to apply
Apply with your CV and any additional information requested. Make the interview process, closing date and any assessment stages clear where known.
Use this as a starting point rather than a final advert. The strongest version will reflect the real role, salary, location, systems, responsibilities, benefits and working arrangements.
What is a Data Analyst role?
A Data Analyst collects, cleans and interprets structured data to help teams make evidence-based decisions. They create queries, transform raw data, produce reports and dashboards, and communicate insights to stakeholders. The role focuses on ensuring data quality, delivering reliable metrics and supporting ad-hoc analysis rather than building production ML models.
Typical level
Entry to mid-level role depending on scope; can report to Head of Analytics, Data Lead or a business function manager.
Core responsibilities
Querying databases, cleaning data, building dashboards, ad-hoc analysis and stakeholder reporting.
Key outcome
Deliver accurate, timely insights and metrics that influence business decisions and improvements.
Typical Data Analyst responsibilities
Responsibilities should reflect the real scope of the vacancy rather than every task someone in this profession might ever complete.
Write
Write and maintain SQL queries to extract data from one or more databases or data warehouses.
Clean
Clean, transform and validate datasets to ensure accuracy and suitability for analysis.
Build
Build and maintain dashboards and regular reports using a business intelligence tool or reporting platform.
Perform ad-hoc analyses
Perform ad-hoc analyses to answer business questions and quantify impact of initiatives.
Define
Define, track and report on key metrics; recommend measurable improvements.
Collaborate with product
Collaborate with product, operations, finance or marketing teams to understand requirements and translate them into data specifications.
Document data sources
Document data sources, transformations and analysis methods; keep analyses reproducible.
Investigate data quality issues
Investigate data quality issues and work with engineering or data teams to resolve root causes.
Data Analyst skills and experience to look for
Keep the essential list focused on what the person really needs to perform the role. Move useful-but-trainable experience into desirable criteria.
Usually worth prioritising
- Proven experience working as a Data Analyst or in a similar role.
- Strong SQL skills for querying relational databases.
- Fluent with spreadsheet tools (advanced formulas, pivot tables) and experience producing clear tables and charts.
- Ability to translate business questions into analytical approaches and to explain results to non-technical audiences.
- Attention to data quality and experience validating and cleaning datasets.
- Good organisational skills and experience documenting work so analyses are reproducible.
Useful where relevant
- Experience with a scripting language for analysis such as Python or R.
- Familiarity with BI tools such as Tableau, Power BI, Looker or similar.
- Experience working with cloud data platforms or data warehouses (e.g. Snowflake, BigQuery, Redshift).
- Knowledge of basic statistics, A/B testing or causal inference techniques.
- Experience with data modelling, ETL processes or working alongside data engineering teams.
- Previous experience in the relevant industry (finance, e-commerce, health, etc.).
See our guide to what to include in a job advert before publishing.
How to write a Data Analyst job advert
Small role-specific details can make the advert much easier for suitable candidates to assess.
Be specific about the tech stack
List the primary database, BI tool and any scripting languages candidates must use day-to-day (for example: Postgres, BigQuery, Power BI, Python).
Give an example of impact
Describe a recent question or project the analyst would handle, such as reducing churn or measuring campaign ROI, to attract candidates with relevant experience.
Clarify the stakeholder environment
State whether the role works with product, finance, marketing or senior leaders — this helps applicants understand the communication and business knowledge required.
Set expectations for assessment
Tell candidates whether you will ask for a take-home exercise, SQL test or a case presentation in interviews so applicants can prepare relevant examples.
Data Analyst salary considerations
Salaries for Data Analysts vary by experience, industry, region and the technical scope of the role. Use these factors to set a competitive band rather than relying on a single market figure.
depending on experience
- Use a genuine range
- Separate variable pay
- Avoid “competitive” alone
- Match salary to seniority
What Data Analyst candidates will want to know
Practical details can influence whether a suitable candidate applies, even when the title and salary are attractive.
What data sources and tools will I be working with?
Experienced candidates want to know the primary databases, BI tools and whether they will have direct access to raw data or must work through engineering teams.
Who are the main stakeholders?
Candidates expect clarity on which teams they will support (e.g. product, marketing, ops) and whether they will present to senior leadership.
Is there support for upskilling or training?
Top candidates look for investment in learning — courses, conferences or time for self-directed learning are attractive.
What is the team structure?
Candidates want to know team size, reporting lines and whether the role sits in central analytics, a function-specific team or within IT/engineering.
What are the working arrangements?
State whether the role is fully remote, hybrid or office-based and any expectations for core hours or on-site days.
Where to post a Data Analyst job
This role suits broad UK job boards and specialist analytics channels. Use general job sites and targeted data/tech boards, plus professional networks such as analytics and data science communities, to reach candidates with the right technical skills. Local university alumni groups and sector-specific forums (finance, health, retail) are useful if you need domain experience.
Data Analyst job description FAQs
Do I have to require a university degree for Data Analyst roles?
A degree in a quantitative subject is helpful but not essential. Many strong analysts demonstrate competence through practical experience, a portfolio of analyses or proficiency tests. Focus on skills and problem-solving evidence when screening candidates.
How should I assess technical skills in interviews?
Combine a short live SQL exercise or take-home task with a discussion of past projects. Ask candidates to explain their approach to cleaning data, choosing metrics and measuring impact — this reveals both technical ability and commercial judgement.
When is Python or R essential?
Treat scripting languages as essential when the role requires advanced analysis, automation or reproducible pipelines. For reporting-focused roles that primarily use SQL and BI tools, Python/R can be desirable but not mandatory.
Should the analyst own ETL or data engineering tasks?
Generally keep ETL ownership with dedicated data engineering where possible. Expect analysts to profile and clean data and to collaborate with engineers on productionising pipelines rather than building large-scale ETL systems themselves.
What interview tasks are reasonable for this role?
Reasonable tasks include an SQL query exercise, a short take-home analysis using a sample dataset, or a case discussion about defining metrics and interpreting results. Avoid long, uncompensated assignments unless necessary and clearly justified.
Ready to hire a Data Analyst?
Use this template to build a clear advert, specify the tools and outcomes that matter most to your team, and share the vacancy across general and specialist channels to attract qualified candidates.
