India has a booming engineering talent ecosystem, concentrated around Bengaluru, Delhi and Mumbai. Many US-based companies find it easier to recruit high-quality and lowest cost talent, and those wages drive compensation from an increasing number of Indian startups. In the US, over time there’s been a big push for salary and compensation (often referred to in India as “package”) particularly with products like levels.fyi. Legacy products like Glassdoor and Payscale are wildly inaccurate.

In this article, we analyze a high fidelity dataset of 25,000 people’s salary information containing:

  • Years of Experience
  • Salary
  • Variable Salary / Bonus
  • Equity / Stock-based compensation
  • Company Name(s)

The data is collected in 2024 from a Y Combinator W21 startup called Weekday that works on recruiting Indian talent. Note that while the data is self-reported, it generally seems in line with anecdotal data from the industry.

Distribution of the total compensations and base salaries of the all employees.
The average total compensation for the 30 most popular companies in the dataset. The error bars actually denote the standard deviation and the numbers are the number of datapoints for that company.
Company Count Avg Total Comp (LPA) Median Total Comp Std Dev Total Comp Median Salary Median Bonus Median Stock
Google 169 66 60 33.4 35 5 20
Gojek 96 59.7 55.5 25.9 42.5 5 5.5
Microsoft 594 58.5 52 28.3 33 5 12
Zeta 74 58 50 31.1 40 3 4.8
Adobe 130 56.3 51.9 26.9 32.5 4 13.5
Salesforce 112 56.1 48 25.4 35 4 10
VMWare 201 53 46 26.9 33 5 7
ShareChat 173 52.1 46 23.2 33.8 3 9
Goldman Sachs 80 51.4 42 27.9 33.5 6 0.1
Paypal 207 50.6 47 22.8 32 3 10
Myntra 82 50.6 46 26.1 32 2 8
Walmart 431 50.4 45 23.3 32 6 5
ServiceNow 82 48.8 40.5 24.9 26.5 3 10
Flipkart 236 45.5 37.4 25.4 27 3 6
Razorpay 96 45 41.5 24.6 32 1.4 5
Oracle 203 44.3 38.5 24.1 26.5 3 10
Amazon 616 44.2 37 26 25 4 5
Tekion 91 43.9 35.5 26.7 27 2 8
Cisco 105 40.8 32.2 23.9 25 3 5
PayTM 141 40.5 31 28.7 25 2 4
Qualcomm 95 38.7 36 16 22 3 8
SAP 103 38.2 32 21.9 23 3 4
Oyo 81 36.1 28 23.2 22 3 4
IBM 69 30.2 24 23 19 1 0
Samsung 72 29.6 24.5 17.6 19.5 2 0
Accenture 133 22.8 18.6 16.5 15.3 2 0
Cognizant 80 18.3 14 14.4 8.1 0.6 0
Infosys 90 18.2 10.9 17.5 10 1 0
Capgemini 78 16.8 12.5 13.8 8.6 0 0
TCS 146 14.9 9.4 12.8 8 0.7 0

Highest Paying Companies

The average total compensation for the 30 highest-paying companies in the dataset. The error bars actually denote the standard deviation and the numbers are the number of datapoints for that company.
Company Count Avg Total Comp (LPA) Median Total Comp Std Dev Total Comp Median Salary Median Bonus Median Stock
Rubrik 34 84.4 83 27.6 43 6.6 32.5
Coinbase 35 84.3 71 25.7 46 5 21
Twilio 15 82.9 73 42.2 50 5.3 16
Uber 54 80.8 76 37.9 46 7.5 20
Atlassian 52 80.6 79.5 35.7 48.5 6.4 20
Rippling 23 79 77 22.6 55 0 20
Games24x7 15 78.1 50 59.1 39 5 8
Twitter 10 76.1 86.5 37.6 45 9 22.5
Khatabook 19 75.3 68 31.2 50.5 5 12.5
UIPath 11 73.7 67.2 26 38 5.7 20
Compass 44 72.1 60 39.7 45 5.1 10
Palo Alto Networks 13 70.9 78 27.2 42 5 16
Dream11 27 69.4 60 30.7 47.5 3.4 10
StockX 13 68.7 53 32 39 5 10
Postman 22 68.3 66.5 33.8 42.5 3.5 10.5
Google 169 66 60 33.4 35 5 20
Meesho 40 62.8 57 29.4 46 2.8 10
CoinDCX 24 61.7 51.5 49 40 2 3
Trell 12 61.7 60 25.6 42.5 4.5 7.5
Intuit 47 61.5 55 27.4 36 5 10
LinkedIn 25 61.2 60.7 32 35 3.7 20
Harness 17 61 67 34.1 36 4 15
Cohesity 18 60.9 53.1 36.3 33 3 16
AppDynamics 16 60.8 48 36 34.5 5 10
InMobi 25 60.5 57.5 31 46 3 8
Interview Kickstart 10 59.8 55.5 33.2 42.5 4.5 8.5
Gojek 96 59.7 55.5 25.9 42.5 5 5.5
Navi 33 59.6 52 34.9 40 4 5
Zeotap 10 58.6 57 20.9 37 5 8.8
Zeta 93 58.6 50 30.6 40 4 5

Lowest Paying Companies

The average total compensation for the 30 lowest-paying companies in the dataset. The error bars actually denote the standard deviation and the numbers are the number of datapoints for that company.
Company Count Avg Total Comp (LPA) Median Total Comp Std Dev Total Comp Median Salary Median Bonus Median Stock
Brototype (Bootcamp) 21 11.1 6.5 8.5 3 2 2
MPhasis 17 13.9 12.5 10.2 12 0.5 0
Crest Data Systems 19 14.4 13 7.1 12 0 0
Tata Consultancy Services 150 14.9 9.4 12.7 8 0.7 0
Cogoport 27 16.7 14 8.4 12 2 0
Capgemini 78 16.8 12.5 13.8 8.6 0 0
Josh Technology Group 18 17.3 17 4.2 12 2 1
Accolite Digital 10 17.4 15.1 8.7 11.1 1.5 0
Infosys 99 17.8 11 16.9 10 1 0
Globant 12 18.2 20.5 8.9 20.5 0 0
Cognizant 80 18.3 14 14.4 8.1 0.6 0
“Fresher” 13 18.3 9.8 17.8 5 2 2
ICICI Bank 11 18.4 16.5 4.9 12 4.5 0
LTI MindTree 50 18.9 14.2 13.7 11.2 0.5 0
Persistent Systems 32 19.9 15.3 14.1 12 1.2 0
CGI 19 20.4 12 15.8 10.5 0.5 0
Cerner 11 20.6 13.1 18.4 8 1.2 0
HashedIn by Deloitte 41 20.7 20 12.9 15 2 0
HCL Technologies 16 21.4 18.8 12.8 15.1 1.6 0
Google Summer of Code 16 21.4 21 9.2 15 3 1.5
Brilio 10 21.5 21.2 12.1 20.2 0 0
Virtusa 17 21.6 22.4 10.5 19.6 0.8 0
Wipro 55 21.9 16 15.3 15 1 0
Jio Platforms 12 22.1 16.5 15.7 13 2 0
Tech Mahindra 30 22.4 22 13.6 16.8 1.5 0
CitiusTech 10 22.5 14.3 16.7 12.8 1.3 0
Accenture 134 22.7 18.6 16.5 15.2 2 0
KoreAI 15 22.9 22 10.4 22 0 0
“Freelancer” 13 23 21.2 21.1 10 0 0
Xoriant 12 23.1 20.2 13.3 20.2 0 0

Years of Experience vs Total Compensation

A valid criticism for the above analysis is that it doesn’t tease apart years of experience as a factor for compensation. Overall, the distribution of years of experience across companies is usually similar enough for comparisons to still be valid. We tried to uncover how experience dictates compensation, but the data was too noisy across companies. Instead, we look at the biggest BigTech multinational companies (MNCs) and look at this relationship within each company: Google, Microsoft, Amazon and Walmart.

The relationship between years of experience and total compensation for engineering in India for the biggest MNCs — Google, Microsoft, Amazon and Walmart.

Data Cleaning

When performing any sort of data analysis, it’s critical to be transparent with how you clean the data. Here were all the steps:

  • Remove outliers: bottom 1% contains monthly salary, and top 2% which contain extra 0s or unrealistic numbers
  • Normalize company names: lowercase, remove punctuation, remove stopwords (corp, india), use the first company when a list is provided, and manually map certain names (“tata consultancy services” -> TCS)
  • Compute Total Compensation: add annual salary, variable salary and annual stock compensation.

This, of course, still has blunders. This data isn’t perfect and I wanted to call out all of the ways it may not answer the questions we’re trying to ask:

  • Self-reported layer can contain outright lies.
  • Outlier pruning isn’t perfect, and there are samples like company “me” that make “170” comp.
  • The samples are biased to the source of the data. It heavily favors younger people looking for jobs, and may not be truly reflective of a company’s true pay philosophy.
  • Not all companies are covered.
  • Stock compensation does not always account for the vesting schedule or the liquidity of the stock, in case of private companies.
  • Stock compensation of private (and public) companies are tied to a certain valuation that may be far from the real valuation at liquidity.
  • Certainty of achieving the annual bonus is not factored in.
  • Normalizing names of companies is far from perfect and may miss data points.

Conclusion

The goal of this effort is to bring more transparency in pay into the Indian market to help employees make betteter decisions about their job. My aim is to make this data freely available and easy to understand.

The entire dataset both raw and processed is available as a published Google Sheet here. Thanks @NirantK for providing the raw source dataset.

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