Analytical judgement
Frames the question, checks assumptions, tests alternatives, and explains limitations before recommending action.
Jumps directly to a chart or technique without confirming the decision or data quality.
Software, Product, Data & AI
Data Analyst Job Description
A Data Analyst turns operational and customer data into reliable reporting, clear explanations, and recommendations that help teams make better decisions.
Edit the candidate-facing template for your organisation, then use the separate recruiter notes to agree the role scope and assess evidence consistently.
Candidate-facing template
Edit the description for your organisation
Replace bracketed details, remove anything that is not genuinely required, and obtain the appropriate internal approval before advertising.
Ready-to-edit job description
Copying excludes all recruiter notes below.
Data Analyst
- Location
- [Location or working arrangement]
- Employment type
- [Full-time, part-time, temporary, or contract]
- Reports to
- [Manager title]
About the role
[Company name] is looking for a Data Analyst to turn business questions into dependable analysis and practical recommendations. You will work with stakeholders to define reporting needs, prepare and examine data, explain findings clearly, and improve how teams use information in day-to-day decisions.
What you will be responsible for
- Work with stakeholders to translate business questions into clear analysis and reporting requirements.
- Collect, clean, validate, and combine data from agreed internal and external sources.
- Build and maintain dashboards, recurring reports, and self-service analysis that people can understand and trust.
- Investigate trends, unusual results, and performance changes, then explain the likely causes and limitations.
- Present findings in plain language and recommend practical next steps to technical and non-technical colleagues.
- Document definitions, calculations, source assumptions, and changes so reports remain consistent over time.
- Partner with data, product, finance, operations, or commercial teams to improve data quality and availability.
- Handle confidential information responsibly and follow the organisation’s data-access and governance requirements.
What success looks like
- Stakeholders receive accurate, timely reporting for agreed decisions and operating reviews.
- Important measures have documented definitions and traceable data sources.
- Recurring analysis becomes easier to maintain and less dependent on manual spreadsheet work.
- Recommendations make uncertainty and data limitations clear rather than overstating conclusions.
Essential qualifications
- Practical experience analysing data to answer real business or operational questions.
- Working knowledge of SQL and confidence querying structured datasets.
- Experience with spreadsheet analysis and at least one reporting or visualisation platform.
- Ability to check data quality, recognise misleading results, and explain analytical limitations.
- Clear written and verbal communication with colleagues who have different levels of data knowledge.
- Organised working habits, including documentation, version control, and careful handling of sensitive information.
Preferred qualifications
- Experience with a language such as Python or R for analysis or automation.
- Familiarity with a cloud data warehouse, transformation tooling, or modern analytics workflow.
- Experience in [relevant sector, product area, or business function].
- A relevant degree, apprenticeship, professional qualification, or equivalent practical experience.
Tools and working knowledge
- SQL and relational databases
- Spreadsheets
- [Power BI, Tableau, Looker, or the organisation’s reporting platform]
- [Python, R, dbt, or other tools genuinely used by the team]
Compensation and benefits: [Add the approved range, currency, benefits, and any location-dependent information required for this vacancy.]
[Company name] welcomes applicants from different backgrounds and will consider reasonable adjustments during the recruitment process and employment, subject to applicable local requirements.
How to apply
To apply, please send [the required application materials] through [application method] by [closing date, if applicable].
Performance expectations
What good performance looks like
Use these outcomes to replace vague activity lists with the evidence the hiring manager expects to see after the person joins.
The analyst can explain which reports and decisions they own, not only which tools they use.
Core measures are defined consistently and stakeholders understand the assumptions behind them.
Manual reporting effort falls as repeatable analysis and documentation improve.
Findings distinguish correlation from causation and make uncertainty visible.
Stakeholders act on recommendations because the analysis is timely, understandable, and connected to their decisions.
Hiring-manager intake
Questions to settle before advertising
Record specific answers so sourcing, screening, and interview decisions use the same definition of the role.
- 1
Which three decisions should this person help the team make more confidently?
- 2
What data sources will they use, and which are known to have quality or access problems?
- 3
Which dashboards or recurring reports will they own during their first three months?
- 4
How much of the role is recurring reporting, investigative analysis, stakeholder work, and data preparation?
- 5
Which skills are essential on day one, and which tools can be learnt after joining?
- 6
Who defines business measures, and who approves changes to those definitions?
- 7
What confidential or regulated information will the analyst handle?
- 8
What would make the hiring manager say the person is succeeding after six months?
Evaluation criteria
Evidence to use in a Data Analyst scorecard
Agree the criteria before interviews begin, then score examples against the same evidence standard.
SQL and data handling
Can describe joins, aggregation, validation, unexpected duplicates, missing values, and how results were checked.
Lists SQL as a skill but cannot explain how they tested whether a dataset or query result was reliable.
Business communication
Explains a complex finding in plain language and adapts the level of detail for different stakeholders.
Focuses on methods and terminology while leaving the decision or recommendation unclear.
Reporting ownership
Documents measures, manages changes, monitors failures, and improves repeatable reporting rather than rebuilding it manually.
Treats dashboard delivery as finished without ongoing quality checks, definitions, or stakeholder adoption.
Responsible data use
Recognises access, privacy, bias, security, and interpretation risks appropriate to the organisation’s data.
Shares or combines sensitive data without considering permissions, necessity, or possible harm.
Structured interview
Data Analyst interview questions and strong signals
Ask the same core questions in the same order, then use follow-ups to understand the candidate’s individual contribution.
1
Tell me about a business question that was initially too broad to analyse. How did you turn it into a workable brief?
Strong answer signal
Clarifies the decision, audience, measure, time period, constraints, and what evidence would change the stakeholder’s action.
2
Describe a time when the data did not support the answer a stakeholder expected.
Strong answer signal
Checks the result, communicates respectfully, shows uncertainty, and helps the stakeholder decide what to do next.
3
How do you check a new dataset before using it in a report?
Strong answer signal
Discusses grain, completeness, duplicates, joins, ranges, missingness, definitions, lineage, and comparison with a trusted source.
4
Which dashboard or report have you improved, and what changed for its users?
Strong answer signal
Connects the change to usage, decision quality, reliability, maintenance effort, or a measurable operating outcome.
5
How would you explain a result that is associated with an outcome but does not prove causation?
Strong answer signal
Uses accessible language, avoids overstating the conclusion, and suggests additional evidence or a suitable test.
6
Tell me about an analysis error you found. What did you do after discovering it?
Strong answer signal
Owns the issue, assesses impact, corrects and communicates it, then improves the check or process that allowed it.
Related titles
Check the scope behind the title
Business Intelligence Analyst — usually places more emphasis on reporting platforms, semantic models, and dashboard delivery.
Product Analyst — usually focuses on product behaviour, experimentation, adoption, retention, and digital customer journeys.
Operations Analyst — usually focuses on service levels, capacity, cost, quality, workflow, and continuous improvement.
Reporting Analyst — may concentrate on recurring reports and data preparation; confirm whether investigative analysis is expected.
Common hiring mistakes
Problems to remove before publishing
Listing every tool in the organisation as essential instead of defining the analytical work the person must perform.
Combining data analysis, data engineering, data science, database administration, and business analysis into one unrealistic vacancy.
Using vague responsibilities such as “provide insights” without naming the decisions, stakeholders, datasets, or expected outcomes.
Requiring a degree when equivalent experience, an apprenticeship, or a strong portfolio would demonstrate the same capability.
Publishing a salary without currency, location, employment basis, or a dependable source and approval process.
Continue the hiring workflow
Related roles and recruiter resources
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Helpful recruiter resources
AI Job Description Generator
Create a tailored first draft from your approved role requirements.
Interview Question Generator
Create follow-up questions after agreeing the evaluation criteria.
Job Order Management Workflow
Move from client or hiring-manager intake into an approved job order.
Job Description Approval Form
Confirm the scope and publishing details before the role goes live.
Source and review notes
How this template was prepared
- Language
- International English
- Prepared by
- ATZ CRM Editorial Team
- Review
- ATZ CRM Recruitment Editorial Review
- Last reviewed
- 2026-08-04
O*NET: Business Intelligence Analysts
Reference for common analytical tasks, knowledge, skills, and work activities.
ESCO: occupations and skills
Reference for international occupation and skills terminology.
Qualifications, compensation, licences, working conditions, and equal-opportunity wording must be checked for the role and location before use.
Recruiter questions
Data Analyst job description FAQs
What should a Data Analyst job description include?
It should explain the decisions the analyst supports, the data and reporting they own, the stakeholders they work with, essential analytical skills, and the outcomes expected. Name tools only when they are genuinely required.
What is the difference between a Data Analyst and a Data Scientist?
A Data Analyst commonly focuses on reporting, investigation, performance questions, and decision support. A Data Scientist is more likely to develop statistical or machine-learning models. Actual boundaries vary, so define the work rather than relying on the title.
Should Python be required for every Data Analyst role?
No. Require Python only when the person will use it for analysis, automation, modelling, or data preparation. For some roles, strong SQL, spreadsheet, visualisation, and stakeholder skills are more important.
How can recruiters assess a Data Analyst without being technical experts?
Ask candidates to explain a real question, the data checks they performed, the limitations they found, and what decision changed. Use an agreed scorecard and involve an appropriate data stakeholder for technical validation.