Data Analyst job description template (India, 2026)
A Data Analyst job description written for Indian companies: responsibilities, requirements, CTC and notice-period framing, five screening questions, and the full text to copy or post on ofper.
Last updated 3 September 2026. Competitor pricing and plan limits are re-verified against each vendor’s own pages.
What does a Data Analyst do?
A company hires its first Data Analyst when the founder's questions — which channel actually makes money, why did retention drop, which city should we expand to — take a week of spreadsheet work to answer and nobody trusts the result. The analyst builds the definitions and the pipelines that make the numbers consistent, maintains the dashboards the leadership reads, answers ad-hoc questions with SQL, and, importantly, tells people when the data does not support the conclusion they want.
The posting should state the data sources — the product database, payment gateway exports, ad platforms, spreadsheets — the tools in use or planned, the stakeholders, and the maturity of the data setup. Analysts from large companies with data engineering teams may expect clean warehouses; an SME analyst builds the cleaning themselves and should know that.
Data Analyst responsibilities
Written as work the person does, not qualities they have. A responsibility a candidate can picture is one they can decide about.
- Define and document the company's core metrics — revenue, orders, customers, retention, acquisition cost, margin — so that every report uses the same definitions.
- Build and maintain the reporting layer: SQL queries or models over [database / warehouse], and dashboards in [Metabase / Looker Studio / Power BI / other] for leadership and teams.
- Answer ad-hoc business questions with analysis that includes the caveats, and present findings so that non-technical stakeholders can act on them.
- Consolidate data from disparate sources — product database, payment gateway, ad platforms, CRM, spreadsheets — with repeatable scripts rather than manual copying.
- Monitor data quality: catch broken pipelines, duplicate records and definition drift before they reach a dashboard.
- Support marketing, sales and operations with cohort, funnel and segmentation analyses, and run the analysis behind experiments.
- Maintain the documentation — metric definitions, data sources, known issues — that makes the numbers trustworthy.
- Propose what to instrument or collect so that next quarter's questions can be answered.
Data Analyst requirements and qualifications
Keep the required list to what you would actually reject a candidate for. Everything else goes under nice to have — a long required list mostly filters out the people you wanted.
- 2 to 5 years as a data or business analyst, with SQL you write daily and at least one dashboard you built that leadership actually used.
- Strong SQL — joins, window functions, CTEs — and advanced Excel or Google Sheets; working knowledge of a BI tool.
- Numeracy and statistical sense: can explain a cohort, a funnel, a confidence interval and why an average is misleading, in plain words.
- Clear written and verbal communication: a one-page analysis with the answer first and the caveats stated.
- Comfort with messy data and the discipline to clean it repeatably rather than by hand.
- Willing to work [from our office / hybrid / remote with overlap hours].
Nice to have
- Python or R for analysis beyond SQL, and familiarity with a modelling layer such as dbt.
- Experience with our domain's data — e-commerce, fintech, SaaS, logistics — and its standard metrics.
- Experience building the first data setup in a company without a data engineer.
Salary, CTC and terms for a Data Analyst in India
State a CTC range. Postings without one get fewer and worse-matched applications, and the range is the first thing an Indian candidate looks for. Say how the CTC is made up — fixed, variable, and any statutory components — so the in-hand figure is not a surprise at offer stage.
- State the CTC range with fixed and variable. Analyst salaries in India span a wide range by company type and tooling; the range gets the right applicants.
- State the tools and the maturity of the data setup honestly; an analyst who expects a warehouse and finds spreadsheets leaves.
- State the work-from-office policy exactly and whether notice buyout is offered.
- State the interview process and whether there is a practical exercise.
Data Analyst job description template
Replace every [placeholder]. Cut anything that is not true of your company — a generic paragraph is worse than no paragraph.
Data Analyst — [Company name], [City] Employment type: Full-time | Location: [City] / [On-site, hybrid or remote] | CTC: [range] per annum | Experience: [years] About the role A company hires its first Data Analyst when the founder's questions — which channel actually makes money, why did retention drop, which city should we expand to — take a week of spreadsheet work to answer and nobody trusts the result. The analyst builds the definitions and the pipelines that make the numbers consistent, maintains the dashboards the leadership reads, answers ad-hoc questions with SQL, and, importantly, tells people when the data does not support the conclusion they want. The posting should state the data sources — the product database, payment gateway exports, ad platforms, spreadsheets — the tools in use or planned, the stakeholders, and the maturity of the data setup. Analysts from large companies with data engineering teams may expect clean warehouses; an SME analyst builds the cleaning themselves and should know that. What you will do - Define and document the company's core metrics — revenue, orders, customers, retention, acquisition cost, margin — so that every report uses the same definitions. - Build and maintain the reporting layer: SQL queries or models over [database / warehouse], and dashboards in [Metabase / Looker Studio / Power BI / other] for leadership and teams. - Answer ad-hoc business questions with analysis that includes the caveats, and present findings so that non-technical stakeholders can act on them. - Consolidate data from disparate sources — product database, payment gateway, ad platforms, CRM, spreadsheets — with repeatable scripts rather than manual copying. - Monitor data quality: catch broken pipelines, duplicate records and definition drift before they reach a dashboard. - Support marketing, sales and operations with cohort, funnel and segmentation analyses, and run the analysis behind experiments. - Maintain the documentation — metric definitions, data sources, known issues — that makes the numbers trustworthy. - Propose what to instrument or collect so that next quarter's questions can be answered. What we are looking for - 2 to 5 years as a data or business analyst, with SQL you write daily and at least one dashboard you built that leadership actually used. - Strong SQL — joins, window functions, CTEs — and advanced Excel or Google Sheets; working knowledge of a BI tool. - Numeracy and statistical sense: can explain a cohort, a funnel, a confidence interval and why an average is misleading, in plain words. - Clear written and verbal communication: a one-page analysis with the answer first and the caveats stated. - Comfort with messy data and the discipline to clean it repeatably rather than by hand. - Willing to work [from our office / hybrid / remote with overlap hours]. Nice to have - Python or R for analysis beyond SQL, and familiarity with a modelling layer such as dbt. - Experience with our domain's data — e-commerce, fintech, SaaS, logistics — and its standard metrics. - Experience building the first data setup in a company without a data engineer. Compensation and terms - State the CTC range with fixed and variable. Analyst salaries in India span a wide range by company type and tooling; the range gets the right applicants. - State the tools and the maturity of the data setup honestly; an analyst who expects a warehouse and finds spreadsheets leaves. - State the work-from-office policy exactly and whether notice buyout is offered. - State the interview process and whether there is a practical exercise. How to apply Apply with your resume through this page. We reply to every application, and shortlisted candidates hear from us within [number] working days.
Opens a free ofper workspace with the title, description and screening questions filled in.
Screening questions for Data Analyst applicants
Five questions is the ceiling on ofper, and it is the right ceiling: a form that asks more than that costs applications. Use them for the things that decide a shortlist, not for anything the resume already says.
| Question | Type | Knockout | Why it earns a slot |
|---|---|---|---|
| Do you write SQL — joins, window functions, CTEs — as a daily part of your work, and have you for at least 2 years? | Yes / No (pass: Yes) | Yes | SQL fluency is the core skill; 'familiar with SQL' on a resume often means having seen it. |
| Have you built a dashboard or report that leadership used to make a decision, and can you describe the decision? | Yes / No (pass: Yes) | No | Not a knockout, but the difference between reporting and analysis; it shapes the interview. |
| Are you able to work [from our City office X days a week / remote with overlap hours]? | Yes / No (pass: Yes) | Yes | WFO settled first. |
| What is your current notice period, in days? | Number (pass: 0 to 90) | No | Timeline planning. |
Questions, answered straight
What should a Data Analyst job description include?
The data sources, the tools in use or planned, the maturity of the data setup, the stakeholders and the questions they ask, the work-from-office policy, the interview process and the CTC range. Analysts decide on tooling maturity and on whether the role is analysis or reporting.
Data Analyst or Business Analyst?
In Indian usage a Data Analyst works primarily in SQL and BI tools on the company's own data; a Business Analyst often means requirements and process work in IT services. If the role is about numbers and dashboards, use Data Analyst and state the SQL requirement.
When does an SME need a Data Analyst?
When the founder's questions take days to answer in spreadsheets and nobody trusts the result, or when marketing spend is significant enough that attribution matters. Usually once there are several data sources — product, payments, ads, CRM — that need consolidating.