Data Scientist Salaries, Data Analyst Salaries, DBA Salaries, etc

Salaries are a taboo topic; nonetheless, compensation in the big data job market has been on the rise and we're very interested in taking a look behind the curtain. The goal of this article is to provide some transparency around the salary landscape for data professionals.

I'm writing this as a data scientist who is frequently solicited by other companies, as well as a team lead who is trying to recruit and build up my own data science team at Wayfair – so I have visibility into both sides of the market. The content of this article is based on (a) my experiences understanding the market from within, (b) our own internal salary data at DataJobs.com, (c) other salaries studies that have been commissioned, and (d) my own initiative to scrape and analyze data from various sources such as Glassdoor.

How to use this Salary Information

Team leaders and recruiters:

The big data job market is an extremely competitive one; you need to make sure to bring the proper weapons to battle. If your compensation is in the bottom quartile or even below median, you'll have issues appealing to people with the skills you desire. Hopefully, the comprehensive view from this article is helpful in setting competitive compensation levels for your current employees, as well as for open job reqs.

Data analysts, data scientists, data engineers, DBAs, etc:

While money isn't everything, it is still an important consideration when figuring out where to make your livelihood. You should be aware if you are being underpaid relative to what the market offers, or if you're at the right level.

A note on salary variance:

Some of the salary ranges we provide have a fairly large spread. This is for good reason — compensation in big data is far from standardized so it is not a good idea to zero in too narrowly. Given the current talent crunch, a salary is really as much as a company is willing to spend. A Hadoop engineer making $110,000 might easily be valued by another company at $145,000. Both are reasonable salaries, but the second company may be in a situation where big data tech development has a larger impact, and thus is willing to pay more for the hire. Many of these jobs in big data tend to have high-variance compensation, as there always seems to be a company out there willing to outbid.

Data Analyst Salary

Data analysts are quant-focused professionals that work hands-on with data, and tend to be at a stage in their career where they are building up their arsenal of tools and developing towards an advanced skill set. Data analysts are potentially 'data scientists in training' or 'analytics managers in training'.

Because it is possible to become a data analyst directly out of school, we will differentiate between entry-level and experienced data analysts. For entry-level data analysts, we refer to individuals who have either a BS or MS degree, but no full-time industry work experience.

Data analysts that have had the chance prove themselves and get promoted through the ranks will command increasing levels of compensation, up through the six-figure threshold.

Notably, the top end of this range blurs with both the data scientist category and analytics manager category, as senior-level analysts deepen their advanced quantitative skills sets as well as gain leadership experience.

Data Scientist Salary

Data scientists are experienced, expert-level professionals in a data-driven company or organization; i.e., there are no entry-level data scientists. Data science salary is generally quite substantial, reaching well into the six-figure range. This is for a couple reasons:

• Data scientists can bring an immense amount of value to the table, by serving as experts in translating complex data into key strategy insight and powerful capabilities. The nature of their work allows them to have a potentially multiplicative effect on the business, rather than just an additive effect as with many other jobs.
• There is a scarcity of professionals with data scientist skills. Not enough talent to go around has led to heavy competition to hire the same set of individuals.

We understand this is a very wide range. Ultimately, there are many variables that make it difficult to get more nuanced; it boils down to level of intelligence, level of experience, as well as what are the unique areas of expertise a data scientist brings with them. Data scientists are expected to have a clear background in statistics/machine learning, but focused depth around certain topics and applications may make a difference in value – are they a neural net expert or NLP expert? If a company has a specific use case for great profundity in a particular area, it may demand a high premium.

Also, is a data scientist a math-only person vs someone comfortable with deep business immersion? People with the latter qualities can better tie advanced algorithms to business value, and are likely able to fetch higher pay. All of these considerations are variables that dictate a more specific range where a data scientist's salary may fall.

There are in fact edge cases of data scientists getting paid over $250,000 in unique situations – e.g. hedge funds, or special cases of advanced algorithm development – but this well above the norm. Overall, though, it is clear that individuals who develop data scientist skills have lucrative opportunities available to them.

Data Science/Analytics Manager Salary

This category comprises of analytics/data science professionals who have risen to a level of managing teams of analysts or data scientists. People in these roles are expected to have sharp technical and quantitative skills in order to speak the same language as their direct reports and earn their respect. Their analytics foundation also gives them the nuanced comprehension to be seasoned architects of major data-driven initiatives. Furthermore, business aptitude and leadership skills are essential to steer their teams strategically.

Roles in this category may have a variety of job titles, such as 'Manager, Analytics and Insights' or 'Director of Data Science'. Overall, the spread in seniority among these managers is broad enough that it makes sense to split them into 3 subgroups, categorized by the number of direct reports. In actuality, the number of reporting employees is not a perfect indication of level, but in our case we will use it as a proxy:

• First-Level Managers (manages 1-3 analytics/engineering personnel)
• Middle-Level Managers (manages 4-9 analytics/engineering personnel)
• Top-Level Managers (manages 10+ analytics/engineering personnel)

Clearly, these numbers reach very high. The trajectory of professionals with deep analytics skills and extensive management experience is without many boundaries. Unfortunately for enterprises that need to hire these senior roles, the supply of individuals that can be both a data science expert and executive leader is very scarce. Filling these positions is often challenging enough that many companies utilize executive recruiting firms to expand their reach. The talent is out there – but everyone is fighting for the same piece.

DBA Salary

Database Administrators are responsible for the upkeep of data systems; they are important assets for any company that relies on database technology. DBAs have technical roles, where level of experience as well as familiarity with different types of technologies certainly affects salary level. Because DBAs may start at the entry level, we'll separate between junior-level and more experienced DBAs:

Especially among experienced DBAs, the complexity of the systems a DBA is responsible for can make a difference in compensation. For example, traditional RDBMS is more basic to work with compared to advanced big data platforms – the keystones being Hadoop and NoSQL technologies. Notably, when working in great depth with advanced NoSQL technology, there is an area where the DBA category blurs with big data software engineer.

Big Data Engineer Salary

Foundationally, big data is enabled by technology. In order for a company to reach the point where big data can solve problems and drive business value, expert engineers are essential in order to architect the data platforms and applications on which all analytical capabilities can function. To use an analogy – these data engineers build and tune the racecar, while data scientists and analytics teams attempt to drive it to victory.

The actual titles for these roles can manifest themselves in many ways, for example:

• Data Engineer
• Software Engineer – Big Data
• Big Data Software Architect
• Hadoop Developer

The systems that these engineers work with are highly sophisticated. Core technical concepts often include: distributed computing and the Hadoop software ecosystem, NoSQL database architecture, data warehousing ETL, etc. Working within these constructs demands seasoned programming ability as well as deep knowledge around information architecture. With the nuanced, rarefied technical skills required to be strong developers in this space, big data engineers are well-compensated for what they bring to the table.

Because level of experience in this space does matter, we will break out this category into two groups:

The wide salary range in the last bucket has to do with both (a) level of seniority and (b) depth of experience with specific technologies. Commonly, many companies have existing big data technology stacks – e.g. MongoDB, Cassandra, Memcached, Redis, etc – and it is optimal to seek out experts who are already well-versed in the enterprise's in-house platforms. This exact-fit technology expert can usually demand a salary premium, launching compensation up to the higher end of the scale. The challenge employers face is that it is not easy to find these perfect matches, thus, companies are usually willing to hire engineers who may not have the exact-fit platform expertise, but are good technical hackers who can learn quickly.

In the broad picture, data engineers are critical contributors that drive forward the technical innovation side of big data. The development from top engineering talent has enabled massively scalable systems and lightning-fast performance, paving the way for powerful new applications and advanced analytic learnings from data.

Special Thanks

The content of this article is guided by our extensive understanding of this space as well as our own internal salary data at DataJobs.com. Additionally, we have gained contributing insight from many other sources and we want to give acknowledgement where it is due – special thanks to InformationWeek, Burtch Works, Glassdoor, KDnuggets, McKinsey Global Institute, and Accenture Institute for High Performance. Materials from these sources were used for research purposes in the gathering of information for this article.

Final Word

In the workplace, salary figures are not transparent or openly discussed, but hopefully this page provides good high-level awareness of the expected compensation for many important roles. This market is booming, and as demand for big data jobs continues to outpace supply of talent, salaries will remain very attractive. Given the dynamic nature of this space, it is a smart practice to maintain a versed sense of the marketplace, in order to understand how to astutely carry a competitive edge.