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Data analyst job description template

A ready-to-post job description for a fresher or intern data analyst. It comes with screening questions for your job post, signals to look for and answers to common hiring questions.

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In short

A fresher data analyst pulls data with SQL, cleans it in Excel or Python and builds reports and dashboards for sales, operations or marketing teams. Ask for SQL, spreadsheets, one dashboard tool and the habit of checking numbers. Any graduate stream works if the candidate can show a real analysis.

Data analyst job description template

Fill in your company details once and both versions below update: the full-time job for freshers and the internship. Copy the one you need into your job post.

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1. Full-time data analyst (fresher)

Data analyst (fresher)

Company: [Company name]
Location: [Location] · Work mode: [Work mode]
Employment type: Full-time
CTC: [CTC for the full-time role]
Joining: [Joining date] · Apply by: [Apply by]

About [Company name]

[About your company]

About the role

[Company name] is hiring a fresher data analyst to join the [Team] team. You will turn raw data from our sales, operations and customer systems into reports and dashboards that managers use every week. You will write SQL, clean and check data, and explain what the numbers say in plain language. A senior analyst will review your work and show you how we define our key metrics.

What you will do

  • Write SQL queries to pull data from the company database, and check the results against the source.
  • Clean raw data in Excel, Google Sheets or Python by removing duplicates, fixing formats and flagging missing values.
  • Build and refresh weekly and monthly reports for sales, operations, marketing or finance teams.
  • Create dashboards in Power BI, Tableau or Looker Studio with clear titles, filters and metric definitions.
  • Answer ad-hoc questions from team leads, such as why a number moved this week, with a short written summary.
  • Run basic analysis such as trends, comparisons and customer segments, and state what the data does and does not prove.
  • Check data quality before each report goes out, and log gaps or errors with the data owner.
  • Document how every report is built, including source tables, filters and formulas, so a colleague can repeat it.
  • Present findings to non-technical colleagues in a few slides or a short email, with one recommended action.

What we are looking for

  • Working knowledge of SQL: SELECT, WHERE, JOIN, GROUP BY and aggregate functions.
  • Comfort with Excel or Google Sheets, including lookups, pivot tables and charts.
  • Basic statistics: average, median, spread and correlation, and why correlation is not cause.
  • Hands-on use of one dashboard tool, such as Power BI, Tableau or Looker Studio, in a course, internship or project.
  • Care with numbers: you check totals, notice odd values and ask when a figure looks wrong.
  • Clear written English and the ability to explain a finding to a reader who is not technical.
  • One analysis project, from college, an internship or your own work, that you can walk us through.

Good to have

  • Python with pandas for cleaning and analysing files too large for a spreadsheet.
  • Experience with web or app analytics tools such as Google Analytics.
  • Familiarity with data exported from a CRM or ERP system.
  • Basic understanding of A/B testing.
  • A public project on GitHub, Kaggle or Tableau Public.

Who can apply

B.Tech, BCA, BSc, BCom, BBA, MBA, MCA and MSc graduates can all apply. The stream matters less than SQL, spreadsheet skills and one analysis the candidate can explain.

How to apply

Apply through this job post by [Apply by]. Shortlisted candidates will hear from us about the next steps.

2. Data analyst internship

Data analyst intern

Company: [Company name]
Location: [Location] · Work mode: [Work mode]
Employment type: Internship · Duration: [Internship duration]
Stipend: [Stipend for the internship]
Joining: [Joining date] · Apply by: [Apply by]

About [Company name]

[About your company]

About the role

[Company name] is looking for a data analyst intern to support our reporting and analysis work. You will clean data, build simple charts and help refresh reports with guidance from a senior analyst. The internship suits pre-final and final-year students and recent graduates who want practice with real business data. [If applicable: strong interns may be considered for a pre-placement offer (PPO), subject to performance and available vacancies.]

What you will do

  • Clean and format datasets exported from our systems, such as order, lead or attendance sheets.
  • Write simple SQL queries, checked by a mentor, to pull data for a report.
  • Update existing weekly reports and dashboards with the latest data.
  • Build charts in Excel, Google Sheets or Power BI to show trends over time.
  • Compare report totals with the source data and flag any mismatch.
  • Write two or three lines of takeaway under each chart.
  • Present one small analysis to the team before the internship ends.

What we are looking for

  • Studying for, or recently completed, a degree in any stream, such as B.Tech, BCA, BSc, BCom, BBA or MBA.
  • Basic Excel or Google Sheets skills: formulas, filters and pivot tables.
  • Introductory SQL from a course or college lab.
  • Interest in how numbers drive business decisions, shown by a class project or a small analysis of your own.
  • Care with detail and honesty about your own mistakes.

Who can apply

B.Tech, BCA, BSc, BCom, BBA, MBA, MCA and MSc graduates can all apply. The stream matters less than SQL, spreadsheet skills and one analysis the candidate can explain.

How to apply

Apply through this job post by [Apply by]. Shortlisted candidates will hear from us about the next steps.

Other titles for this role

Junior data analyst, Associate data analyst, Data analyst trainee, Analytics executive, Reporting analyst, BI analyst (entry level).

Screening questions to add to your job post

  1. What is the difference between WHERE and HAVING in SQL? A quick test of real query writing. WHERE filters rows before grouping and HAVING filters groups after it, and people who have only read about SQL often mix them up.
  2. Two reports show different sales totals for the same month. What is the first thing you would check? Good answers start with the date range, filters, duplicate rows or a different metric definition before blaming the data.
  3. Name one Excel feature you have used on a messy file, and what it solved. Look for a specific feature, the problem it solved and how the candidate checked the result. College and personal projects count.
  4. What would you do with a column that is empty for many rows? Looks for judgement: asking the data owner, checking whether the gaps follow a pattern and noting the choice, instead of quietly deleting or filling.
  5. Which chart would you use to show monthly sales for three regions, and why? Tests chart sense. A line chart with one line per region is easier to read than three pie charts, and the reason matters more than the answer.
  6. Describe one college, personal or internship analysis and the question it answered. Share a link only if you have permission. Look for a clear question, the candidate’s own contribution and a finding. Accept a description or a private walkthrough instead of a public link.

What to look for when you shortlist

  • A project that starts with a question and ends with a finding, not only a dashboard built from a course template.
  • SQL the candidate can explain, including joins and grouping, using a personal project or a made-up example rather than confidential employer queries.
  • Notes on data checks, such as duplicates removed, blanks handled and totals matched to the source.
  • Charts whose titles state the message, with labelled axes and no decoration.
  • A plain-language summary under each analysis, written for a manager and not for another analyst.
  • Questions about how your business uses the numbers, which show real curiosity about the work.

Mistakes to avoid in this job description

  • Asking for machine learning in a reporting role: list what the analyst uses weekly (SQL, spreadsheets, one dashboard tool) and move the rest to good to have.
  • Not saying where the data lives: tell applicants whether the analyst will query a database, use exported sheets or both, so they know what to prepare.
  • Judging by a dashboard screenshot: a polished dashboard may come from a course template, so ask what question it answers and how the candidate built it.
  • Leaving the first deliverable vague: name the weekly report the new analyst will take over in month one, so candidates can picture the work.
  • Writing ‘strong analytical skills’ with nothing to test it: add one SQL question and one question about checking a number.

Questions about hiring a data analyst

What should a fresher data analyst know?

Four things: SQL to pull data, spreadsheets for quick checks, one dashboard tool to share results, and basic statistics. Add the habit of matching totals to the source before a report goes out. Python is a bonus unless your team already works in notebooks. A small analysis the candidate can walk you through counts for more than a long list of course certificates.

How is a data analyst different from a data scientist or a business analyst?

Pick by the problem you have. If it is ‘we cannot see or trust our numbers’, hire a data analyst, whose output is reports and dashboards. If it is ‘we want to predict what happens next’, you need a data scientist who builds models. If it is ‘teams disagree on what the system should do’, a business analyst writes the requirements. In small firms one person may cover two of these.

Do I need Python in a fresher data analyst job description?

Only if the analyst will use it in the first months. For reporting from a database and spreadsheets, SQL and Excel are enough, and a Python requirement shrinks your applicant pool. If the first tasks involve maintaining Python scripts, list Python with pandas as a must-have. Otherwise, keep it under good to have and provide training.

How can I test a fresher data analyst quickly?

Send a small sheet of orders or leads with two questions, such as the top product by revenue and the change from last month. Plant one error, such as duplicate rows. Ask for the answers plus a two-line note on anything they would flag. The note tells you more than the numbers, because it shows whether they checked the data before trusting it.

Where can I find fresher data analysts?

Start with placement cells and department heads in statistics, computer science, commerce and management colleges. You can also post the role on LineupX, which is free for employers and has no cap on the number of jobs. Attach the screening questions from this page, so applicants show their SQL and number sense before you shortlist.

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