Data Analyst Salary in Hyderabad 2026: What Moves an Analyst's Pay

Search for data analyst salary in Hyderabad and you will find tidy ranges for freshers and experienced analysts, almost never with a source. We could not find a published, dated study that measures analyst pay in Hyderabad, so this guide does not quote one. Instead it looks at something more useful for planning: which choices go with higher analyst pay in real data.

The data is the 2025 Stack Overflow Developer Survey (Stack Overflow survey 2025), with pay converted to US dollars at the 25 June 2025 exchange rate (survey methodology). To compare fairly, each person's pay is divided by the median of their peers: people in the same country with similar years of experience. A value of 1.00 means paid like the typical peer.

import pandas as pd

# 2025 Stack Overflow Developer Survey: employed respondents who reported annual pay
so = pd.read_csv("results.csv", low_memory=False)
paid = so[so.Employment.eq("Employed") & so.ConvertedCompYearly.gt(0)].dropna(subset=["WorkExp"]).copy()
countries = paid.Country.value_counts()
paid = paid[paid.Country.isin(countries[countries >= 30].index)].copy()
paid["stage"] = pd.cut(paid.WorkExp, [-1, 2, 5, 10, 60])
# Each person's pay divided by the median of peers in the same country and experience band (1.00 = peer median)
paid["vs_peers"] = paid.ConvertedCompYearly / paid.groupby(["Country", "stage"], observed=True).ConvertedCompYearly.transform("median")

analysts = paid[paid.DevType.eq("Data or business analyst")]
print(f"A1 data and business analysts: {len(analysts)} in {analysts.Country.nunique()} countries; median vs peers {analysts.vs_peers.median():.2f}")
print("   analysts who used a language in the past year vs those who did not:")
for lang in ["SQL", "Python", "R"]:
    used = analysts.LanguageHaveWorkedWith.fillna("").str.split(";").apply(lambda items: lang in items)
    print(f"   {lang:<7} used by {used.sum():>3}: {analysts[used].vs_peers.median():.2f} | others: {analysts[~used].vs_peers.median():.2f}")

print("\nA2 everyone in the survey, by industry (median vs same-experience peers, industries with 300+ people)")
by_industry = paid.groupby("Industry").vs_peers.agg(people="size", vs_peers="median")
print(by_industry[by_industry.people >= 300].sort_values("vs_peers").round(2).to_string())

india = paid[paid.Country.eq("India")]
print("\nA3 India only, by industry (20+ people)")
by_india = india.groupby("Industry").vs_peers.agg(people="size", vs_peers="median")
print(by_india[by_india.people >= 20].sort_values("vs_peers").round(2).to_string())

print("\nA4 analysts and related roles (median vs same-experience peers)")
for role in ["Data or business analyst", "Data scientist", "Data engineer", "Developer, back-end", "Project manager"]:
    group = paid[paid.DevType.eq(role)]
    print(f"   {role:<26} {len(group):>5} people: {group.vs_peers.median():.2f}")

A1 data and business analysts: 144 in 34 countries; median vs peers 0.74
   analysts who used a language in the past year vs those who did not:
   SQL     used by 105: 0.79 | others: 0.64
   Python  used by 101: 0.79 | others: 0.68
   R       used by  38: 0.84 | others: 0.71

A2 everyone in the survey, by industry (median vs same-experience peers, industries with 300+ people)
                                            people  vs_peers
Industry                                                    
Higher Education                               640      0.74
Government                                     759      0.87
Manufacturing                                  803      0.89
Other:                                        1566      0.95
Healthcare                                     883      0.98
Media & Advertising Services                   465      1.00
Retail and Consumer Services                   637      1.00
Insurance                                      303      1.00
Transportation, or Supply Chain                563      1.00
Software Development                          8270      1.01
Energy                                         434      1.01
Internet, Telecomm or Information Services     960      1.05
Computer Systems Design and Services           435      1.06
Banking/Financial Services                     840      1.10
Fintech                                       1034      1.18

A3 India only, by industry (20+ people)
                                            people  vs_peers
Industry                                                    
Software Development                           579      0.97
Healthcare                                      27      1.00
Retail and Consumer Services                    20      1.07
Banking/Financial Services                      36      1.12
Other:                                          32      1.21
Transportation, or Supply Chain                 21      1.40
Fintech                                         59      1.48
Internet, Telecomm or Information Services      40      1.50

A4 analysts and related roles (median vs same-experience peers)
   Data or business analyst     144 people: 0.74
   Data scientist               288 people: 1.03
   Data engineer                409 people: 1.09
   Developer, back-end         3241 people: 1.08
   Project manager              112 people: 1.00

Analysts start behind, so the levers matter

The 144 analysts in the survey earned a median of 0.74 times their same-experience peers. That gap is why the levers below matter so much for analysts: small differences in skills or employer move them a long way relative to where they start.

Lever 1: write code

Analysts who had used SQL in the past year earned 0.79 times their peers' median, against 0.64 for those who had not. For Python, it was 0.79 against 0.68, and for R, 0.84 against 0.71. These groups are small, and the survey cannot prove that learning SQL raised anyone's pay; analysts who code may also work in better-paying teams. But the direction is consistent across all three languages, and it matches what job descriptions ask for. An analyst who can only use spreadsheets and dashboards competes for the lowest-paid part of the market.

The practical order is SQL first, because nearly every analyst job uses it, then Python for automation and analysis beyond what SQL does well. The SQL queries analysts need daily is a good place to start.

Lever 2: choose the industry

Across everyone in the survey, the industry made a clear difference even after adjusting for country and experience. Fintech (1.18) and banking and financial services (1.10) paid the most; higher education (0.74) and government (0.87) the least. Software development itself sat at the middle, 1.01.

In India, the same pattern appeared on smaller groups: fintech (1.48), internet and telecom (1.50) and banking (1.12) above software development (0.97). With 20 to 60 people per industry outside software, these are indicative, but the direction agrees with the global result.

For an analyst, industry can be easier to change than title, and the domain knowledge you build in finance, such as risk, payments and customer behaviour, travels with you to the next employer.

Lever 3: the next role

The analyst role itself is the lowest-paid of the related roles in the survey. Against same-experience peers, project managers sat at 1.00, data scientists at 1.03, back-end developers at 1.08 and data engineers at 1.09, against 0.74 for analysts. That does not mean every analyst should leave the role: the spread within every role is wide, so a strong analyst can earn more than a typical data engineer. But it does mean that the skills which connect analysis to engineering or modelling, SQL at scale, Python, data pipelines and statistics, are also the skills that open better-paid roles. Building them keeps both doors open: a stronger analyst position now, and a move later if you want one. The analyst to data scientist roadmap describes one such move step by step.

What it means in Hyderabad

The survey has no city field, so these are national and international patterns, not Hyderabad figures. They describe what tends to go with higher pay, not what a given company offers. For a specific offer, use local evidence: postings that publish a pay band, the band a recruiter gives for the level, and analysts already in the team. Compare fixed pay first, because variable pay and one-time bonuses behave differently.

If you are choosing between two analyst offers in different industries, weigh the industry as well as the number. The survey suggests that the industry you build experience in shapes later pay, so a slightly lower offer from a bank or fintech can be worth more over a few years than a higher one in a sector that pays below its peers. Ask both employers how analysts there have progressed, and what the band for the next level is.

Limits of this data

This is a developer survey, so analysts who code are probably over-represented, and analysts are only 144 of over 18,000 people with pay data. The survey shows association, not cause. Pay was converted on one date, which is why only ratios appear.

For the bigger picture of how analyst pay compares with data engineers and scientists, see data engineer salary compared with other roles. For growing in the role, see the data analyst career path, and for a raise or a new offer, the salary negotiation guide.

SQL, Python and analysis for analysts are taught in the Data Analytics course.

Sources

• 2025 Stack Overflow Developer Survey — Stack Overflow
• 2025 Developer Survey methodology (Stack Overflow) — Stack Overflow