Data Analyst.
Find the story inside a company's numbers — why sales dipped, which customers are leaving, what to do next. The most realistic first door into India's data industry: no entrance exam, no mandatory engineering degree, tools you can learn in months. But “data is the new oil” hype hides a real shift — this is the honest version.
A data analyst turns raw business data into decisions using SQL, Excel and Power BI or Tableau. No entrance exam, no mandatory engineering degree — skills and a portfolio decide. Typical fresher offers in India are ₹3–6L a year; top product companies and GCCs pay ₹7–13L at entry. By year 3–5, most analysts earn ₹9–14L. Senior analysts at top firms reach ₹40–48L.
Open sqlbolt.com and finish the first interactive SQL lesson. In your browser, free, zero setup.
SQL is the single most-asked skill in Indian data analyst job descriptions. Fifteen minutes tonight tells you more about whether this career fits you than a week of videos.
Is data analyst worth it in India? The 30-second answer
Before everything else, the truth about this career in three lines.
- You'll answer business questions with data — pull numbers with SQL, build dashboards in Power BI or Tableau, and explain to decision-makers what the numbers mean and what to do about them.
- It's the lowest entry bar in the data world: any degree works, the core tools (Excel, SQL, one BI tool) are learnable in 6–10 months, and BFSI, e-commerce, consulting and GCCs all hire freshers. The catch: low bar means heavy competition, and certificates alone don't get you hired — a portfolio and a passed SQL test do.
- The honest AI caveat: routine pull-numbers-refresh-dashboard roles are exactly what AI is compressing. The analysts who thrive pair SQL with business judgment — and use the analyst seat as a launchpad into data science, product or analytics leadership.
What does a data analyst actually do?
Every company you know — Swiggy, HDFC Bank, a hospital chain, a D2C brand on Instagram — generates data all day: orders, payments, clicks, cancellations, complaints. Somewhere in that pile are answers to the questions the business is losing sleep over. The data analyst is the person who finds them.
In practice that means four repeating moves: get the data (write SQL queries against the company's databases), clean it (real data is always messy — duplicates, gaps, typos), analyse it (Excel, Python, statistics — find the pattern), and tell the story (a dashboard, a chart, a one-slide answer a manager can act on).
The last move is the one that decides your career. Two analysts can run the same query; the one who can stand in front of a sales head and say “we're losing repeat customers in tier-2 cities, here's why, here's what I'd do” is the one who gets promoted. This is a business job that uses technical tools — not a coding job that happens to touch business.
9:30 am to 6:30 pm — what's it actually like?
Based on an analyst with 2–3 years of experience at an e-commerce company in Bangalore. Roughly.
- 9:30Check the morning dashboardsYesterday's orders, revenue, cancellations. One number looks off — note it down.
- 10:00Standup with the teamWhat everyone's analysing this week, which stakeholder requests came in overnight.
- 10:30Chase the anomalyWrite SQL to dig into the odd number. Turns out a payment gateway failed in two states for three hours.
- 12:00Stakeholder requestThe category manager wants to know why a product line's returns doubled. Scope the question properly before touching data.
- 13:00LunchWith the product and ops folks — half the job is knowing what the business is worrying about.
- 14:00Deep analysis blockReturns data: clean it, segment it, find that one warehouse is shipping a defective batch.
- 16:00Build the storyTurn the finding into three charts and one recommendation slide. Rehearse the 90-second version.
- 17:00Present to the category headFindings land. Action agreed. This meeting is why the role exists.
- 17:45Maintenance & wrap-upUpdate a weekly dashboard, document the returns analysis, plan tomorrow.
Reality check: junior years have more routine reporting and less detective work. Month-end and festival-sale weeks bring crunches. The detective days are what you grow into.
The honest test — before you commit a year of your life.
Don't pick this because LinkedIn says data is hot. Pick it because the way it works fits you.
- You enjoy puzzles — 'why did this number change?' genuinely interests you
- You like being the person in the room who actually knows the facts
- You're comfortable with numbers without needing to be a math topper
- You can explain complicated things simply
- You're patient with messy, boring cleanup work — because the answer is under it
- You like business questions as much as technical tools
- Spreadsheets and screens all day drain you
- You want to build products, not analyse them — that's engineering
- Repetitive work makes you careless — junior years have plenty of it
- You hate presenting or defending your work to senior people
- You need every day to look different — reporting cycles repeat
- You expect a ₹15L offer in year one — this career pays in years 3–6
What does a data analyst actually earn (in India)?
Course-seller ads quote the ceiling as if it were the average. Here's the full distribution. All figures are annual CTC.
| Experience | Role | Pay range |
|---|---|---|
| 0–2 yrs | Junior / Reporting Analyst Most fresher offers land between ₹3L and ₹6L — Accenture/Wipro/Infosys-class offers sit at ₹3.5–4.5L, mid-size analytics firms at ₹5–7L. Top product companies and GCCs pay ₹7–13L at entry (levels.fyi India), but those seats are few and fiercely contested. | ₹3L–₹13L/yr |
| 2–5 yrs | Data Analyst By 3–5 years, most analysts earn ₹9–14L (national average). Switching jobs once here can yield 40–60% hikes. This is the fastest growth window — Google's L3 analyst comp in India runs around ₹20L median. | ₹6L–₹30L/yr |
| 5–8 yrs | Senior Data Analyst Senior analyst averages around ₹12L nationally; strong seniors at product companies clear ₹18–35L. Amazon's L5 data analyst band tops out near ₹48L total comp (levels.fyi) — the realistic ceiling for staying an analyst at a top firm. | ₹12L–₹48L/yr |
| 8–12 yrs | Analytics Manager / Lead Leading a team of analysts. ₹20–40L is the common band; analytics leadership at top consulting and product firms crosses ₹60L — a top-slice outcome, not the median. | ₹18L–₹60L/yr |
| The pivot | Data Scientist / Product roles Many of the best-paid 'analysts' stopped being analysts — they moved into data science, product analytics or product management, where the ceiling keeps rising. The analyst seat is the launchpad. | ₹15L–₹60L+/yr |
Bengaluru, Hyderabad, Gurgaon and Mumbai pay 15–20% above the national average. Skills move the needle more than city: analysts who add Python to SQL + BI tools command a visible premium at every level.
The largest employer. HDFC, ICICI, Paytm, Axis, insurers and NBFCs run credit-risk, fraud, collections and customer analytics teams. Commerce and economics graduates are actively sought here.
Funnel, pricing, supply-chain and retention analytics. Fast-moving, high-ownership, best place to become a product analyst.
Client-facing analytics projects across industries. Good training ground; modest first pay.
Global Capability Centres of foreign banks, retailers and tech firms run analytics for global operations from India. Strong pay and structured growth.
Most SME and mid-market analyst jobs in India are still Excel-heavy — pivot tables, VLOOKUP/XLOOKUP, conditional logic, charts. SQL + Power BI is the upgrade that unlocks better-paying roles at analytics firms, GCCs and product companies.
Python is not required for most fresher analyst roles. It becomes genuinely useful for automation (cleaning scripts, scheduled reports), ML-adjacent work (feature engineering, model monitoring), and GCC roles where the data volumes outgrow Excel. Learn it in months 6–10 of your roadmap — not before you have SQL and a BI tool locked in.
Will this still be a good career in 10 years?
Honest answer: yes for analysts who carry business judgment — but the routine-reporting layer of this job is being automated right now. Here's the calibrated version.
First, the real demand: it's broad, not hype. Banks and insurers run on credit-risk and fraud analytics, e-commerce companies on funnel and supply-chain analytics, hospitals on clinical and operations data, and the global capability centres (GCCs) of foreign banks and retailers in Bengaluru, Hyderabad and Gurgaon hire analysts in volume. Unlike pure software roles, analyst demand is spread across every industry — that breadth is the career's real moat.
Now the part course-sellers won't tell you: the bottom layer of this job is being automated.AI features inside Power BI and Tableau, text-to-SQL tools, and automated reporting now do much of what a junior “dashboard jockey” was hired for — pull the numbers, refresh the report, email the summary.
Roles that were only that are shrinking, and the fresher bar has risen: companies expect new hires to arrive with SQL, a BI tool and a portfolio already in hand, not learn them on the job.
What AI can't do is the top half of the job: knowing which question matters, judging whether the data can be trusted, and persuading a business to act. Analysts who pair SQL with that judgment are getting more valuable, because they now produce in a day what took a week. Plan for that job — not the report-refreshing one.
- · Analysts who own a business domain (risk, growth, supply chain)
- · Product analysts at startups and product companies
- · BFSI and GCC analytics teams
- · Analysts fluent with AI tools — they ship 5x faster
- · The analyst → data scientist / PM pipeline
- · Pure report-refresh / MIS-only roles
- · “Dashboard jockey” seats with no business contact
- · Manual data-entry-plus-Excel hybrid jobs
- · Certificate-only freshers with no portfolio
- · Analysts who never moved past one tool
“AI can refresh the dashboard. It can't tell the sales head which question to ask — or be trusted when the answer is expensive.”
Stream, degree — and what doesn't matter.
B.Tech, B.Sc (Maths/Stats), BCA, B.Com or Economics all work — analytics teams in BFSI actively like commerce and economics graduates because they understand the business. Keeping Maths in Class 11–12 (any stream) makes the statistics easier later.
There is no exam gate and no licence. Working analysts came from history, BA English, even pharmacy — what got them hired was a portfolio of real analyses and passing the SQL test in the interview. The route is open; it just takes more self-discipline.
- An engineering degree or an IIT tag — any graduate can compete
- Advanced calculus — school-level maths plus statistics intuition covers most of the job
- An expensive ₹2–4L bootcamp — free and low-cost resources cover the entire skill set
- Machine learning — that's the data scientist's bar, not yours (yet)
- Perfect English — clear beats fancy; charts do half the talking
What it'll cost you to actually get there.
Any working laptop + free resources (SQLBolt, Kaggle, Microsoft Learn) + optionally a Coursera certificate (a few thousand rupees). The cheapest professional career to train for.
B.Com / B.Sc / BCA / BBA at a normal college. The degree opens the HR filter; the tools you self-learn alongside it get you the offer.
Optional, not required. Buy structure and placement help if you need it — but read placement claims with the same scepticism you'd apply to any ad.
From starting SQL to first offer, alongside or after a degree. Faster with a referral or internship; slower if you collect certificates instead of building a portfolio.
The tools are the easy part. The hard parts are statistics intuition and learning to think in business questions — both come from doing real analyses, not watching videos.
How to become a data analyst (in India) — step by step
The order matters more than the pace: Excel → SQL → BI tool → Python. Tools first, portfolio always.
Foundation
Months 0–2- Master Excel properly: pivot tables, VLOOKUP/XLOOKUP, charts, conditional logic. Most Indian analyst interviews still test it.
- Learn statistics basics: averages vs medians, distributions, percentages, growth rates, correlation vs causation.
- Analyse one real dataset end to end in a spreadsheet — your own expenses, cricket stats, anything you care about.
- Get comfortable asking 'compared to what?' — the analyst's reflex question.
Core tools
Months 2–6- Learn SQL — the single most-asked skill in Indian analyst JDs. SELECT, JOIN, GROUP BY, window functions.
- Pick ONE BI tool and go deep: Power BI (more Indian JDs) or Tableau. Don't learn both at once.
- Build 2–3 dashboards on real public datasets (Kaggle, data.gov.in) — each answering a stated business question.
- Practice SQL daily on free sites (SQLBolt, then StrataScratch/LeetCode SQL easy sets).
Python & portfolio
Months 6–10- Learn Python with pandas: loading, cleaning, joining, simple automation. You don't need ML.
- Build a 3-project portfolio where each project states a question, shows the analysis, and ends with a recommendation.
- Publish everything: GitHub for code, LinkedIn posts for the story version. Recruiters do look.
- Learn to use AI tools for drafting queries and cleaning scripts — then verify every line. Interviewers test whether you understand your own work.
Get hired
Months 10–18- Apply broadly: BFSI, e-commerce, consulting, GCCs, mid-size analytics firms — and don't snub 'MIS Executive' or 'Reporting Analyst' titles; they're real entry doors.
- Expect 100+ applications in the current market. Referrals and visible portfolio work cut that dramatically.
- Prepare for the actual interview loop: a live SQL test, an Excel/case exercise, and 'walk me through your project'.
- Take a decent first offer and learn the business deeply — the ₹9–14L band opens at job two, not job one.
SQL questions, case rounds, and what a real portfolio looks like.
Course ads show you the skills. Here's what hiring managers in BFSI, e-commerce and consulting actually ask.
- Month-on-month loan disbursement growth
- Find customers with 3+ missed payments in the last 6 months
- Rank branches by NPA (non-performing asset) ratio using window functions
- Identify duplicate transaction IDs in a payments table
- Calculate 7-day rolling average of daily orders
- Find users who placed an order in month 1 but not month 2 (churn)
- Top 5 categories by return rate, filtered to orders > ₹500
- Write a funnel query: visits → add-to-cart → purchase conversion
- Pivot monthly sales data from rows to columns
- Find the second-highest salary per department
- Identify gaps in a date sequence (missing days in a report)
- Join three tables: customers, orders, products — aggregate revenue per customer segment
This is not a software portfolio. You don't need a deployed app or a GitHub full of code. You need evidence that you can find a business insight and explain it clearly.
Pick a real dataset (e-commerce orders, bank churn, IPL data). Run an analysis in a Jupyter notebook. The last cell should be a plain-English paragraph: what you found and what a business should do about it. Publish the notebook publicly on Kaggle.
Build a 3–5 chart dashboard on a topic a hiring manager would recognise — sales performance, customer segmentation, supply-chain delays. Share the live link. Recruiters can click it without installing anything.
A folder with your schema, your queries (with comments explaining the business question each answers), and the output chart as a PNG. Shows you can write clean, documented SQL — not just screenshots.
Data analyst vs data scientist vs data engineer — which door is yours?
Every student confuses these three. They share the word 'data' and almost nothing else about the entry bar. Here's the honest comparison.
- Tools
- SQL, Excel, Power BI/Tableau, light Python
- Math level
- School maths + statistics intuition
- Entry pay
- ₹3–6L typical; ₹7–13L top firms
- Entry bar
- Lowest — any degree + portfolio + SQL test
Answers business questions with existing data. Freshers are genuinely hired into this role across BFSI, e-commerce, consulting and GCCs.
- Tools
- Python, ML libraries, statistics depth, SQL
- Math level
- Real statistics + linear algebra basics
- Entry pay
- ₹6–14L — when you can get in
- Entry bar
- High — few true fresher seats; degree filters common
Builds models that predict — churn, fraud, demand. Most Indian data scientists became one after an analyst or engineering stint, not straight from college.
- Tools
- Python/Java, deep SQL, Spark, Airflow, cloud
- Math level
- Low — it's engineering, not statistics
- Entry pay
- ₹5–8L entry; ₹8–20L mid
- Entry bar
- Software-engineer bar — coding interviews apply
Builds the pipelines that move and store data. Hired and interviewed like a backend developer — if this appeals, compare it with the developer path too.
Why analyst first:it's the only one of the three that hires freshers in volume, from any degree, on tools learnable in under a year. Data scientist postings mostly want experience or a master's; data engineering runs you through software-engineering interviews. Start as an analyst, earn while you learn the business, then pivot upward from inside — that's the route most working data scientists in India actually took.
Where it can take you in 10 years.
Typical-path figures for a steady climber — not the full spread in the salary table above, where top-company comp pushes ceilings higher. Two long-term tracks: go deeper into data (scientist) or wider into the business (manager / product).
Learn the company's data inside out. Automate your own routine work. Volunteer for the analyses nobody owns.
Own a domain — risk, growth, supply chain. Switch jobs once; this is where the 40–60% hikes live.
Stakeholders come to you with the hard questions. Choose your fork: deeper (data science) or wider (leading analysts).
Run a team, own the analytics roadmap, sit in business reviews. Your value is judgment now, not queries.
Director-level analytics, or the pivot paths fully realised — data science leadership or product management.
Six very different lives — all data analyst.
HDFC, ICICI, insurers, NBFCs. Credit risk, fraud, customer analytics. The largest analyst employer — and the most stable.
Accenture, TCS, Deloitte, EY. Client analytics projects. Easiest fresher door, modest first pay, great training ground.
Flipkart, Swiggy, Zepto, Meesho. Funnels, pricing, supply chain. Fast, high-ownership, the best place to become a product analyst.
GCCs of Walmart, JPMorgan, Tesco, airlines — analytics for global operations, run from Bengaluru/Hyderabad/Gurgaon. Strong pay and process.
Hospital chains, pharma majors, health-tech. Clinical, operations and market analytics — a quieter, growing niche.
Dashboards and reporting for D2C brands and SMBs. Viable side income once skilled; rarely the first job.
The honest trade-offs.
- · Lowest entry bar in the data industry — any degree, no exam
- · Among the cheapest professional careers to train for
- · Demand spread across every industry, not just tech
- · Clear pivot paths: data science, product, analytics leadership
- · Skills transfer — a BFSI analyst can move to e-commerce
- · Sane hours compared to consulting, CA or medicine
- · You see your work change real decisions
- · Modest first pay — ₹3–6L is the realistic fresher band
- · Heavy competition at entry; certificates alone don't cut it
- · Junior years contain real drudgery: cleaning, repeat reports
- · Routine-reporting roles are actively being automated
- · Ceiling as a pure analyst is lower than developer/data scientist — the big money needs a pivot
- · Your insights can be ignored — influence takes years to build
- · Month-end and sale-season crunches
What people get wrong about this career.
Data is the new oil — any data course guarantees a job.
The demand is real; the guarantee is not. Certificates are a commodity now — every applicant has one. What gets you hired is a portfolio of real analyses and passing a live SQL test. Treat course ads promising ₹12L placements the way you'd treat any ad.
You need an engineering degree or to be a coding genius.
Any degree works — BFSI analytics teams actively hire B.Com and Economics graduates. SQL is learnable in months, and the maths is school-level plus statistics intuition. The bar is consistency, not brilliance.
AI will make data analysts obsolete.
AI is automating the routine layer — pulling numbers, refreshing dashboards, drafting summaries — and roles that were only that are shrinking. But deciding which question matters, checking whether data can be trusted, and persuading a business to act remain human work. The job is shifting up, not disappearing.
Data analyst is just a cheaper data scientist.
Different jobs. Analysts answer business questions with existing data; scientists build predictive models and need real statistics and ML depth. The analyst door is the one actually open to freshers — and it's the most common route into data science later.
You'll be earning ₹15L within a year.
Typical fresher offers are ₹3–6L; ₹7–13L happens at top product companies and GCCs for a small, fiercely contested slice. The strong money arrives at years 3–6 — the 2–5 year window is where 40–60% job-switch hikes live.
Who actually makes it — and how?
Composite stories drawn from common Indian analyst paths — including the median one, not just the highlights. Names changed.
“I was an MIS executive in Indore making reports nobody read. I learned SQL and Power BI in evenings over eight months. Got a ₹5.2L analyst role at an NBFC in Pune. Three years and one switch later I'm at ₹11L doing credit-risk analytics.”
₹11L by year 3“B.Tech in mechanical, no coding background. Did the Google Data Analytics certificate, then realised it wasn't enough — built three real projects and ground through SQL practice. Around 120 applications, four interviews, one offer: ₹4.2L at a services firm. Not glamorous. The portfolio for job two is already underway.”
₹4.2L after ~120 applications“Joined a quick-commerce startup at ₹6L as their second analyst. Ended up owning all funnel metrics, sat in every product review, learnt A/B testing on the job. Moved to product analyst, then senior. Year six: ₹28L — and the PM track is open.”
₹28L by year 6Other careers this path also opens.
Most people who start as analysts don't retire as analysts — and that's the point. These are the natural pivots your SQL, statistics and business judgment open up.
The classic ladder. Two-plus years of analyst SQL and business context, plus Python statistics and ML on the side, is the most common real-world route into data science in India.
If you enjoy the pipelines more than the presentations — go deep on SQL, Python, Spark and a cloud platform. Entry is ₹5–8L, mid is ₹8–20L, and senior/staff DEs reach up to ₹35L. Data engineers are harder to find than analysts and paid accordingly.
Analysts who own product metrics (funnels, retention, A/B tests) at startups become product analysts, then PMs. Knowing what the data says is the rarest PM skill.
Marketing teams run on GA4, attribution models and campaign dashboards. An analyst who understands marketing data becomes the growth-team hire every D2C brand wants.
For commerce-stream analysts pulled toward the finance side of the data: CA is the credentialed version, and analytics-skilled CAs are the most in-demand slice of that profession.
If you find yourself enjoying the Python scripts more than the dashboards, you're halfway to engineering — SQL and APIs carry straight over.
One concrete action — based on where you are right now.
Doesn't matter what stage. The hardest part is starting; the rest is just continuing.
Open Google Sheets and analyse something you care about — your cricket team's scores, your family's monthly expenses. Make one chart that answers one question. That's the whole job in miniature.
Keep Maths if you can — it smooths the statistics later. Tonight: finish the first free lesson on sqlbolt.com in your browser. Fifteen minutes, zero setup.
Start the Excel → SQL → Power BI sequence now and build one portfolio project per semester. Apply for analytics internships from year 2 — including 'MIS' and 'reporting' internships; they count.
Run the roadmap above at full speed: SQL daily, one BI tool deep, three portfolio projects in 4–6 months. Apply broadly across BFSI, e-commerce and consulting while you build.
You have an unfair advantage: domain knowledge. A banker who learns SQL becomes a risk analyst; an ops executive becomes a supply-chain analyst. Block 6–8 hours a week for 8–12 months and pivot inside your own industry first.
The shortlist. No fluff.
Hundreds of resources exist. These are the ones working analysts actually recommend.
- SQLBoltFreeInteractive SQL in the browser — the first thing to do
- Kaggle Learn + datasetsFreeFree micro-courses and real datasets for your portfolio
- Microsoft Learn — Power BIFreeOfficial, free, structured path to the most-asked BI tool
- Khan Academy — StatisticsFreeThe statistics intuition layer, explained properly
- freeCodeCamp — Data Analysis with PythonFreePandas and cleaning, free with a certification
- data.gov.inFreeIndian public datasets — portfolio projects that stand out
- Google Data Analytics (Coursera)PaidStructured beginner path; useful for the HR filter, not sufficient alone
- StrataScratch / LeetCode SQLPaidReal interview SQL questions — the actual test you'll face
- Storytelling with Data (book)PaidThe presentation half of the job, taught properly
- Maven AnalyticsPaidProject-based Power BI / Tableau / SQL courses
Data analyst in India: quick answers
The questions people actually search — answered straight.
- What does a data analyst earn in India?
- Freshers typically earn ₹3–6 lakh a year, rising to ₹7–13 lakh at top product companies and GCCs. By 3–5 years most analysts earn ₹9–14 lakh, and senior analysts at top firms reach ₹40 lakh or more. All figures are annual CTC.
- Is data analyst a good career in India in 2026?
- Yes — it is one of the lowest-cost entries into a well-paid white-collar career, with no expensive degree or entrance exam, and almost every bank, hospital chain, e-commerce and consulting firm hires analysts. The caveat: routine reporting is being automated, so the analysts who thrive keep adding skills like Python on top of SQL.
- Do you need a degree to become a data analyst in India?
- No specific degree is required — SQL skill beats degree pedigree. It is the single most-asked skill in Indian data analyst job descriptions, and B.Com or B.Sc graduates reach the same desks as engineering graduates. A strong portfolio matters more than the name on your certificate.
- Which skills do data analysts need in India?
- SQL first, then Excel and a BI tool such as Power BI or Tableau. Adding Python to SQL commands a visible pay premium at every level, and business judgment — knowing which question to ask and whether the numbers can be trusted — is what gets analysts promoted.
A note to read with your parents.
The honest answers to the questions every Indian parent quietly worries about.
Is it stable?
Yes, with one honest caveat. Every bank, hospital chain, e-commerce company and consulting firm in India now employs analysts — BFSI alone is one of the largest hirers. The caveat: purely routine reporting roles are being automated, so stability belongs to analysts who keep learning. The skill set (SQL, Excel, BI tools) also transfers across industries, which is its own safety net.
Does it pay well?
It pays a middle-class salary fast and a strong one with patience. Typical fresher offers are ₹3–6 lakh a year — modest — but mid-career analysts earn ₹9–14 lakh, senior analysts at top companies reach ₹40 lakh or more, and the role opens doors to data science and product management where pay climbs further. The cost of entry is among the lowest of any professional career.
Will AI take this job?
AI is genuinely automating the routine parts — pulling numbers, refreshing dashboards, drafting summaries. Roles that were only that are shrinking. But companies still need a person who knows which question to ask, whether the numbers can be trusted, and what the business should do about them. The realistic risk is a higher bar for the first job, not the career disappearing.
What about lifestyle?
Office or hybrid desk work, standard hours most of the year, with crunches around month-end reporting or big launches. No night shifts in most roles, no physical strain, and the job exists in every metro — your child doesn't have to move to one specific city.
What about the 'log kya kahenge?' question
Data analyst is a respected white-collar profession at recognisable companies — banks, Big Four consulting firms, Amazon, Flipkart. It needs no expensive degree and no entrance exam, and it's one of the few careers where a B.Com or B.Sc child can reach the same desks as engineering graduates.
Figures on this page were last reviewed on 13 June 2026 by the Path10x Editorial Team. Exam statistics, seat counts and pay scales change every cycle — always confirm against the official notification before acting. Compiled from:
- AmbitionBox — Data Analyst salary in India (fresher and average bands)
- Glassdoor India — Data Analyst salaries (mid and senior bands)
- levels.fyi India — Data Analyst compensation by level (entry, overall, senior)
- Naukri.com — Data Analyst jobs India (tool demand, JD scans)
- NASSCOM — Analytics and AI talent report (sector demand)
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