Data Engineer Salary in Hyderabad 2026: How the Role Pays Compared With Others

Data engineer salary pages for Hyderabad usually give a range for each level with no source. We could not find a published, dated study that measures data engineering pay in Hyderabad, so this guide does not quote invented numbers. It answers a more useful question with real data: compared with people doing other jobs in the same country, with the same experience, how well does data engineering pay?

The data is the 2025 Stack Overflow Developer Survey (Stack Overflow survey 2025), whose annual pay figures were converted to US dollars at the 25 June 2025 exchange rate (survey methodology). Each person's pay is compared twice: with the median in their country, and with the median of their peers, people in the same country and the same experience band. The second comparison matters: roles differ in how experienced their people are, and experience drives pay.

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", "DevType"]).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], labels=["0-2", "3-5", "6-10", "11+"])

# Two comparisons: against everyone in the same country, and against peers in the same country AND experience band
paid["vs_country"] = paid.ConvertedCompYearly / paid.groupby("Country").ConvertedCompYearly.transform("median")
paid["vs_peers"] = paid.ConvertedCompYearly / paid.groupby(["Country", "stage"], observed=True).ConvertedCompYearly.transform("median")

roles = ["Data engineer", "Data scientist", "Data or business analyst", "AI/ML engineer",
         "Developer, back-end", "Developer, full-stack", "DevOps engineer or professional", "Cloud infrastructure engineer"]
sub = paid[paid.DevType.isin(roles)]
table = sub.groupby("DevType").agg(people=("vs_peers", "size"), median_years=("WorkExp", "median"),
                                   vs_country=("vs_country", "median"), vs_peers=("vs_peers", "median"))
print(f"R1 {len(paid):,} employed respondents in {paid.Country.nunique()} countries; pay relative to medians (1.00 = median)")
print(table.sort_values("vs_peers").round(2).to_string())

de = paid[paid.DevType.eq("Data engineer")]
print(f"\nR2 data engineers: {len(de)} people; share paid above their peers' median: {(de.vs_peers > 1).mean():.0%}")
print("   middle half of data engineers, relative to peers:",
      f"{de.vs_peers.quantile(0.25):.2f} to {de.vs_peers.quantile(0.75):.2f}")
print(f"   data engineers in India in the sample: {(de.Country == 'India').sum()}")

print("\nR3 data engineers who used a tool in the past year vs those who did not: median pay relative to peers")
for column, tool in [("DatabaseHaveWorkedWith", "Snowflake"), ("DatabaseHaveWorkedWith", "Databricks SQL"),
                     ("PlatformHaveWorkedWith", "Amazon Web Services (AWS)"), ("PlatformHaveWorkedWith", "Kubernetes"),
                     ("PlatformHaveWorkedWith", "Terraform"), ("LanguageHaveWorkedWith", "Scala")]:
    used = de[column].fillna("").str.split(";").apply(lambda items: tool in items)
    print(f"   {tool:<27} used by {used.sum():>3}: {de[used].vs_peers.median():.2f} | others: {de[~used].vs_peers.median():.2f}")

R1 18,621 employed respondents in 61 countries; pay relative to medians (1.00 = median)
                                 people  median_years  vs_country  vs_peers
DevType                                                                    
Data or business analyst            144           9.5        0.71      0.74
Developer, full-stack              5812          10.0        0.90      0.93
Data scientist                      288           8.0        0.97      1.03
DevOps engineer or professional     603          12.0        1.11      1.05
Developer, back-end                3241          10.0        1.10      1.08
Data engineer                       409          10.0        1.06      1.09
Cloud infrastructure engineer       226          11.0        1.20      1.16
AI/ML engineer                      287           8.0        1.17      1.26

R2 data engineers: 409 people; share paid above their peers' median: 58%
   middle half of data engineers, relative to peers: 0.83 to 1.35
   data engineers in India in the sample: 25

R3 data engineers who used a tool in the past year vs those who did not: median pay relative to peers
   Snowflake                   used by  93: 1.13 | others: 1.08
   Databricks SQL              used by  88: 1.15 | others: 1.05
   Amazon Web Services (AWS)   used by 186: 1.13 | others: 1.04
   Kubernetes                  used by 122: 1.20 | others: 1.03
   Terraform                   used by 118: 1.16 | others: 1.06
   Scala                       used by  55: 1.27 | others: 1.06

What the comparison shows

Data engineering pays above the typical developer. Against peers with the same experience in the same country, the median data engineer earned 1.09 times the peer median, ahead of back-end developers (1.08), data scientists (1.03) and full-stack developers (0.93).

The analyst gap is large. Data and business analysts earned 0.74 times their peers' median, the lowest of the eight roles. For analysts, moving into data engineering is one of the clearer routes to higher pay that the data supports.

AI/ML and cloud roles pay more still. AI/ML engineers (1.26) and cloud infrastructure engineers (1.16) came out highest. Many data engineers move towards these, through ML pipelines and platform work.

The experience adjustment changes the picture. Compared only with their country's median, AI/ML engineers looked similar to cloud engineers (1.17 against 1.20). Against same-experience peers, the AI/ML premium is clearer, because AI/ML engineers in the sample were less experienced, a median of 8 years against 11. Always ask whether a role pays more, or just employs more senior people.

Inside the role, the spread is wide. The middle half of data engineers earned between 0.83 and 1.35 times their peers' median, and 42% earned below it. The title alone does not guarantee a premium; skills, employer and negotiation decide where in that range you land.

Which skills go with higher pay

Among data engineers, those who had used certain tools in the past year earned more relative to their same-experience peers. The clearest gaps were for Scala (1.27 against 1.06 for those who had not used it), Kubernetes (1.20 against 1.03) and Terraform (1.16 against 1.06). AWS, Databricks and Snowflake users were also ahead, by smaller margins.

Read this carefully. The groups are small, from 55 to 186 people, and the survey cannot tell whether the tool raises pay or whether better-paying employers simply use it. The pattern is still consistent: the higher-paid data engineers tend to work on platforms and infrastructure, such as Spark in Scala, containers and infrastructure as code, not only on SQL and pipelines. If you want to move up within the role, those are the directions the data points to.

What it means in Hyderabad

The survey has no city field, and only 25 data engineers in India answered, too few for India-only figures. So these are international patterns, adjusted for each country. They tell you how the role ranks, not what a Hyderabad company will offer. To price a specific offer, use local evidence: job postings that publish a band, recruiters' stated bands for a level, and people already in the role.

Reading an offer

Indian offers are usually quoted as a total cost to company, which mixes components that behave very differently:

• Fixed pay: the part you can count on every month. Compare this first.
• Variable pay: depends on company and individual performance; ask what share was actually paid out last year.
• Retirement contributions and gratuity: part of the total, but not in your monthly pay.
• Joining bonus and stock: often one-time or vesting over years; check the clawback and vesting terms.

Two offers with the same total can differ a lot in fixed pay. The salary negotiation guide covers how to respond.

Limits of this data

The survey is self-selected and leans towards experienced developers. Roles are self-reported, and "data engineer" covers different jobs in different companies. Pay was converted on one date, which is why only ratios are shown. The analysis covers employed people only, not contractors or freelancers. These numbers are a fair way to compare roles, not a price list.

For pay growth by experience stage, see data scientist salary from fresher to senior. To get into the role, see how to become a data engineer and the data engineering roadmap.

The data engineering skills behind these roles are taught in the Data Engineering course.

Sources

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