6 Essential Types of Data Professionals
Data Roles

6 Essential Types of Data Professionals

Shawn Fergus
Shawn Fergus

Explosive. Interesting. The Wild Wild West.

These three terms capture how Bernhard Schroeder, writing for Forbes in 2021, described the profession of data analytics and employment. Telling, as this was four years after the Bureau of Labor Statistics concluded demand for data professionals was clearly outpacing supply.

Frankly, this is great news for data professionals in general. But for business stakeholders, owners, and human resource (HR) professionals, this explosive, interesting, Wild Wild Westification of data roles presents unique challenges. And not all of these challenges involve simple considerations of supply and demand.

Turns out, we’re in the midst of a movement.

The rise of the data professional

As the field of data workers evolves to keep up with data demands, so too do the relevant skill sets, technical skills, and programming languages involved in each of these specialized professional roles.

This continued evolution then complicates the interviewing process, compensation strategies, and decision-making around new hires. Nailing down the specifics of these roles grows difficult as the roles themselves evolve to keep up with demand.

Additionally, workforce data suggests this is much more than a passing fad. For example, the Bureau of Labor Statistics also estimates that the demand for Data Scientists alone will grow 36% from 2021 to 2031. To put this in perspective, a rate of just 5-8% is generally regarded as the range for average job growth, across industries.

So, considering the BLS projection is more than four times as much, it’s safe to say we’re looking at some explosive growth regarding data professionals. And this is why, for those who don’t want this phenomenon to pass them by, it’s imperative to understand the seven critical roles at play here, and the variety of skills and wages related to each.

6 essential types of data professionals in 2023

1. Data engineers

Despite how much we hear about it, big data doesn’t simply happen. Systems that handle data management (i.e. systems that can collect, store, and make raw data accessible) need to be designed, built, and improved over time. And data engineers are the people who get it done.

For these reasons, data engineering is a foundational role on any given data team. Data engineers work with other data professionals, like data scientists and business leaders, to help an organization make better decisions.

It follows, then, that data engineering skills are also foundational, allowing the data engineer to function as the go-to operator on data teams. These include a mastery of SQL and coding (e.g., Python), a solid understanding of operating systems like Linux, UNIX, Windows, and Solaris, a firm grasp of data architecture, data warehousing, data automation, and the ability to leverage Apache Hadoop-Based Analytics.

According to Payscale.com, data engineers earn anywhere from $67,000 to $134,000 annually, with a median wage of $94,406 per year:

Image source: Payscale.com

Additionally, Payscale lists Ruby, MapReduce, Oracle, JavaScript, and Amazon Redshift as the top five skills that affect data engineering salaries in 2023:

Image source: Payscale.com

Payscale’s aggregated data also shows a gender breakdown of 75.5% male to 23% female in the role, with 1.5% opting to self-define. And prospective candidates in San Francisco, New York, and Seattle may encounter wages higher than the national average.

2. Data scientists

If engineers build the roads of big data in business, data scientists are the ones driving on them. Data scientists need to employ lateral thinking to uncover business insights within the big data that data engineers enable.

Doing so requires the average data scientist, more so than the average data engineer, to operate a tad closer to the day-to-day goals of the business. After all, the data science tools they wield to go after those insights—prediction engines, optimization algorithms, and more—depend on business-specific goals. So, while data scientists need to be versed in programming languages like Python and SQL, their skillset also needs to encompass the manipulation and visualization of data. Further, data scientists often get the job of figuring out how to capture data so it can be analyzed in the first place.

Modern Data Scientists must also share proficiencies in Hadoop, a common, open-source tool for managing large datasets from multiple repositories, and visualization tools like D3.js, ggplot2, and Tableau.
Payscale.com shows that data scientists currently earn anywhere from $70,000 to $137,000 annually, with a median wage of $98,602 per year:

Image source: Payscale.com

Additionally, Payscale lists C++, cybersecurity, research analysis, image processing, and PyTorch Software Library as the top five skills that affect data scientist salaries in 2023:

Image source: Payscale.com

Payscale’s current gender breakdown for data scientists comes in at 68% male and 31% female, with 1% preferring to self-define. And, much like data engineers, roles offered in San Francisco and New York currently offer wages above the national average.

3. Data analysts

Much like data scientists, the role of data analysts also involves working with data. However, while data science often involves creating new ways to capture and analyze data, data analysts focus on finding patterns, trends, and insights within existing data.

Doing so can involve organizing, cleaning, and visualizing datasets, in addition to using statistical analysis and software tools such as Excel or Tableau to uncover trends in the data they analyze.

This means that professionals in these roles are highly skilled in statistical analysis and data visualization, and they're able to communicate the results of their data analysis to non-technical stakeholders.

Turning again to Payscale info, data analysts in 2023 are earning between $47,000 and $89,000 per year, with $65,000 being the annual average:

Image source: Payscale.com

As for skills that most directly affect data analyst salaries, JMP Statistical Software, data architecture, Git, Microsoft Azure, along with consulting management rank highest:

Image source: Payscale.com

Worth noting as well, the gender gap among data analysts is much closer than that between data engineers and data scientists. While still not quite in the majority, nearly half (43.5%) of more than 7,000 Payscale respondents were female, compared to 55.3% male and 1.2% preferring to self-define.

However, regional differences in salary are similar to those listed above, with data analysts in San Francisco reporting higher wages than the national average, followed by New York.

4. Machine learning engineers

Where data analysts combine different disciplines and systems to learn from data, machine learning engineers combine software engineering and data knowledge to create machines that can learn from data with little or no human oversight (AKA, deep learning).

"Machine learning" is a catch-all phrase for these algorithms, without which artificial intelligence (AI) would still be the stuff of science fiction. And, being directly involved in the creation, testing, and tuning of algorithms means machine learning (ML) engineers need to be incredibly comfortable with mathematics and statistics.

At the same time, those working in these roles need to champion the human users that machine learning algorithms are designed to work with. They are also often responsible for deploying and optimizing the software and tools they've coded into existence.

Payscale data shows a range of $78,000 to $156,000 for machine learning engineers, with the average coming out to $113,561:

Image source: Payscale.com

Skills affecting machine learning engineers deviate from others on this list as well, with image processing topping the list, followed by development operations (DevOps), Scala, reinforcement learning, and data modeling:

Image source: Payscale.com

For this newer role amongst data professionals, 79.1% of 591 respondents identified as male, followed by 19.9% as female and 1% preferring to self-identify. Like our other essential roles, those seeking employment in either San Francisco or New York can expect to see salaries above the national average.

5. Business intelligence analysts

Business intelligence (BI) analysts use data to develop insights, look for trends, and create reports, much like data analysts do. The roles differ though—BI's goal is to help business leaders and stakeholders make better decisions based on their work. Doing so requires a deeper and more holistic understanding of business operations than the role of most data analysts requires.

That said, in helping to steer the business, BI analysts will commonly use tools that overlap with other data professionals, like Excel or Tableau. They will, however, use a variety of unique tools, including Power BI, Qlik, Spotfire, and Cognos.

Data from Payscale pegs the range for BI analysts between $53,000 and $97,000 per year, with $71,860 being the annual average:

Image source: Payscale.com

The full spectrum of tools BI analysts use is further illustrated by the top skills affecting their salaries, with data processing and BusinessObjects at the top of the list, followed by Oracle Business Intelligence Enterprise Edition (OBIEE), market analysis, web analytics, and Amazon Redshift:

Image source: Payscale.com

Payscale’s self-reported gender data shows a split of 62.3% male to 36.8% female, with .8% preferring to self-identify. Salary expectations also differ from our previous roles detailed here, with BI analyst salaries in New York, Seattle, and Austin, Texas coming in above the national average.

What is a BI Analyst and How Do I Become One?

6. Database administrators

Finally, database administrators (DBAs) get a lot of credit for keeping on the big data lights. This ensures other roles like those listed above can do the work they do. It's the DBAs who, like data engineers, help design and create database structures. But DBAs are also responsible for maintaining the database and ensuring it can meet the needs of the business.

These responsibilities include setting up and organizing user access controls, handling database security, maintaining and improving database performance, and planning redundancies to ensure valuable data is backed up and recoverable in emergencies.

In order to perform this mission-critical role, DBAs need a thorough understanding of database fundamentals, including mastery of SQL, relational database design principles, and data normalization methodologies.

Professionals in this role must also constantly stay ahead of the latest database innovations, news, and developments. Not only do they need to maintain their own systems, but they must deter the near-ubiquitous influence of bad external actors.

Payscale data shows professionals in this role make from $49,000 to $113,000 annually, with the average being $77,000 per year:

Image source: Payscale.com

And those either in or considering working in the role should specifically pay attention to skills such as database management, NoSQL, Sybase, and Sybase Adaptive, as these are ranked highest as positive salary factors:

Image source: Payscale.com

The gender split for DBAs is also closer than some other data professional roles, with a 62.1% to 36.9% split between men and women, with 1% preferring to self-define. DBA salary expectations are also less centralized than other roles, with Washington, DC barely clocking in above the national average.

Data professions: A modern-day gold rush

If it’s true that data analytics and employment are the Wild West then the professional push to fill related positions could be described as a modern-day gold rush. Stories of success and riches to be had are inspiring more and more people to clamor for a piece of the action.

Despite some roles emerging as “essentials” it’s wise to remember that it took an army of individual contributors, in a variety of roles, to create a holistic, sustainable economy. As the demands of big data continue to grow, the potential of other roles, like those found in DevOps, cyber security, quality assurance (QA), and talent recruitment are not to be slept on.

To those already working in these roles, don’t ignore the fact that services and solutions like those at Shipyard also continue to evolve, to help professionals like those listed above keep up with demand. For a sense of what’s possible, browse our growing library of versatile solutions, or simply sign up for a free trial to see for yourself.

In the meantime, please consider subscribing to our guided tour through data roles led by people doing the jobs. The series is called "Captain's Compass" and you can learn more about it and sign up for alerts by following this link.

Learn how to Navigate the Career Data Seas with "Captain's Compass" - Trailer