Wondering about good data careers? Compare career paths, responsibilities, and salary information for data analysts, data scientists, and data engineers.
This article was produced in collaboration with Iris González.
Data careers are a relatively recent phenomenon, albeit one that receives much attention from industry thought leaders. In fact, a few years back, the Harvard Business Review famously called data science “the sexiest job of the 21st Century.”
We suspect that amusing title might be up for debate with some other professions, but one thing we know for certain?
Data is everywhere. And in every field.
With organizations awash in massive amounts of raw data, decision-makers in every industry are clamoring to understand the insights in all that information.
Consequently, with high demand for data literate employees, a data science career can be a terrific entry point into practically any public or private sector field.
Yet despite what many people believe, one need not always have an expensive, highly specialized degree from an elite school to gain access to these desirable, high-paying data careers. Training opportunities are available.
And, no matter where you live or the industry in which you now work, you can find ways to pursue data-related career opportunities.
In fact, there’s likely to be a very good data career available if you:
In the rest of this article, we’ll answer the common questions about data careers, specifically:
Plus, we'll talk briefly about how employers can help develop data talent and increase organizational data literacy.
First, however, let's talk about why data jobs can lead potentially to lucrative, engaging career paths.
There are three main reasons:
1. Many industries leverage data to drive innovation.
Below is a small representative sample of organizations that rely upon data:
Also, other established and emerging industries that rely heavily upon advanced technologies (e.g., robotics, autonomous vehicles, aviation) use data to drive innovation.
2. Businesses face mounting challenges when it comes to organizing their data.
3. There's a shortage of skilled data talent.
Although data analysts, data scientists, and data engineers share similar titles, the ways they each approach data are slightly different and therefore not interchangeable. (Note: There are other data career paths—business intelligence analysts, machine learning engineers, etc.—but this article focuses on those most widely known.)
Simply put, data analysts help address business problems using data tools and techniques.
Typically, data analysts hold entry-level positions. They may work either on data teams or serve as functional analysts embedded in any organizational unit.
Data analysts create and maintain data systems and databases. Also, they may also fix coding errors. They pay particular attention to patterns and trends that can be useful for predictive and diagnostic analytics. And they interpret data using statistical tools.
Data analysts:
Because companies increasingly generate lots of data, they look to data analysts to find significant patterns that can drive business decisions for:
Proficiency in programming languages such as Python and understanding the fundamentals of data handling, reporting, and modeling are part of a data analyst's skill set.
They also need to know how to use data visualizations and infographics to translate insights into easy-to-understand, actionable recommendations.
With enough experience and some additional training, individuals may choose to advance from a data analyst position to become a data scientist or data engineer. Plus, as an added bonus for many people, data analysts often have the opportunity to work remotely.
According to Indeed, the average annual base pay for a U.S. data analyst is approximately $66,000.
Although many data analysts work remotely, compensation continues to vary considerably by geographic region just as it rises over the course of one’s career.
To give you more insights into income potential, below are the ten U.S. locations with the highest concentration of data analysts and the corresponding median compensation (or median salary), which includes base salaries for people at various experience levels (including entry level staff through to more seasoned professionals).
DATA ANALYSTS: METRO AREA MEDIAN COMPENSATION*
*Source: Emsi Burning Glass – economicmodeling.com
There are several points of entry to data analyst positions, and some professionals may learn on the job. Others may train individually or through corporate upskilling or reskilling initiatives.
Did you know?
Correlation One's Data Science for All (DS4A) is a training program designed to help people who identify as Women, Black, Hispanic / Latinx, and/or LGBTQ+ start their careers in data science, data analytics, and data engineering. In the U.S., each DS4A program offering (Empowerment, Women, Data Engineering) is provided 100% free-to-learner thanks to the support of our generous Employer Partners.
As a mid-level professional, data scientists manage large complex datasets using machine learning and predictive analysis.
They work with “Big Data”— larger, more complex data sets that are so large that traditional data processing software can't manage them.
Data scientists must develop algorithms that can help collect and clean data and models that show the relationship between different kinds of data.
A degree in statistics, computer science, or mathematics is helpful but not necessarily required.
Data scientists usually:
Through training, data scientists learn to use advanced tools to work on large volumes of data from different sources such as (but not limited to):
They then sort the data according to the business use case.
In comparison with data analysts, data scientists usually spend a lot more time preparing raw data—a process that generally cannot be automated—and less time building models.
Data scientists use computer programming languages such as:
Knowing how to work with big data frameworks like Hadoop, Spark, and Pig is also helpful.
Building a model alone isn't enough, however. Data scientists also need domain expertise, strong problem-solving skills, and the ability to communicate results and recommendations effectively to organizational peers as well as more senior leaders.
And, in case you were wondering, data scientists may be be able to work remotely or in hybrid workplaces. (It all depends upon the company or industry that employs them.)
According to Indeed, the average annual base salary for a U.S. data scientist is $117,000.
And, as with data analyst positions, compensation continues to vary considerably by geographic region just as it rises over the course of one’s career.
So, to give you further insights into earning potential, below are the ten metro U.S. locations with the highest concentration of data scientists—and the corresponding median compensation (or median salary), which includes base salaries for people at various experience levels (including entry level staff through to more seasoned professionals).
DATA SCIENTISTS: METRO AREA MEDIAN COMPENSATION*
*Source: Emsi Burning Glass – economicmodeling.com
Data scientists typically have more advanced training and experience than data analysts. Still, many of them get started through basic data training programs, too, and then increase their data knowledge on the job, through additional training and coursework, or by obtaining higher education degrees.
Depending upon the company and amount of experience an individual has, data engineers are mid- to senior-level professionals.
Companies need data engineers to take large amounts of data, clean it up, and make it usable. Data analysts and data scientists can then analyze the data to uncover actionable recommendations that may add profitable business value.
Data engineers:
Data engineers store, pre-process, and make large sets of business data usable for other teams across the organization. They create the data pipelines that collect the data from multiple resources, transform it, and store it in a more usable form for data analysts and scientists to use in predictive models.
Data engineers support a company's data science team by building procedures to help with data mining, modeling, and production. Their participation is crucial in improving data quality in an enterprise's overall data pipeline and architecture.
Wondering if data engineers can work remotely? Yes, some can. Others work in hybrid or on-site positions. It depends upon the industry or employer.
According to Indeed, a data engineer in the U.S. can expect to earn a base salary of around $122,000.
And, as with data analyst and data scientist positions, compensation continues to vary considerably by geographic region just as it rises over the course of one’s career.
To give you further insights into potential earnings, below are the ten metro U.S. locations with the highest concentration of data engineers—and the corresponding median compensation (or median salary), which includes base salaries for people at various experience levels (including entry level staff through to more seasoned professionals).
DATA ENGINEERS: METRO AREA MEDIAN COMPENSATION*
*Source: Emsi Burning Glass – economicmodeling.com
Given the rigorous nature of data engineering technology, training and skill development is essential to land a data engineering position.
Did you know?
Data Science for All (DS4A) / Engineering is a FREE, 17-week training program for talented individuals from underrepresented communities (including Black, Hispanic/ Latinx, Veterans, LGBTQ+, among others) funded by our generous Employer Partners.
Many employers offer tax-free tuition reimbursement (up to $5,250 per year currently) for courses and degrees.
There's another data-specific training solution for employers, too. In addition to working with our DS4A Employer Partners, Correlation One helps companies—including Amazon and Softbank—upskill their employees in data analytics and related areas (e.g., data literacy, business intelligence, logistics and supply chain).
The enterprise-level data training we offer is advantageous not only for employees but also employers because:
From unleashing innovations to improving performance, data literacy holds the key to unlocking success in practically every industry. Yet talent shortages in data science persist as more and more employers seek to hire these specialists.
With training, preparation, and practice, however, good career paths in data analytics, data science, and data engineering can provide individuals with lucrative, in-demand jobs in today’s data robust economy—and for years to come.