Michael Bowers, author and Chief Data Architect at FairCom Corporation, initially set out to research three careers in his presentation titled Data Architect vs. Data Modeler vs. Data Engineer for the DATAVERSITY® Data Architecture Online 2019 Conference. The process brought him to a wealth of information he would have appreciated much earlier in his career, so Bowers was inspired to expand his comprehensive analysis to eight career positions: data analyst, data quality engineer, database administrator, data modeler, BI engineer, data engineer, data scientist, and data architect.
Throughout his presentation, Bowers highlighted opportunities for businesses to save money by hiring strategically, as well as ways that individuals looking to increase their salaries can make wise choices about what skills and abilities they develop.
Bowers has held or supervised all eight positions in the last 25 years and used that experience, as well as online research, to inform his findings, assisted by colleague Rob Pederson, People Ops and HR Leader at FairCom. They used job descriptions from indeed.com, glassdoor.com, and payscale.com, as well as Bowers’ personal experience. They discovered that Payscale generally projected lower salary results, and Indeed showed higher results, with Glassdoor.com in between, so they used Glassdoor as the primary salary information source. He pointed out that the salary ranges he used in his presentation all have very high confidence ratings, due to the high number of participants who shared salary and bonus information with Glassdoor.
The average base pay for data analyst is around $67,000 a year, with additional cash compensation of about $4600 a year, he said. The low end of the range, $46,000, represents analysts hired just out of college, with senior and principal data analysts in the $96,000 range. Data analysts serve on the front lines, writing reports, fixing problems, and reconciling data across business systems, making them highly valuable to the business side. Often assigned to provide data for a manager on the business side, they manually search through business systems, find answers, and produce reports using tools such as Excel or Access.
Data Quality Engineer
Data quality engineers are at the lower end of the pay scale, and usually work on the IT side. They are responsible for finding bad data and cleaning it up so that the analysts, BI engineers, and data engineers on the IT side can be more successful. Data quality engineers make it possible for data scientists to do their work without having to do Data Quality themselves, and that’s a key to saving money, he said, because:
“Data scientists spend 80 percent of their time doing Data Quality, and data scientists make a lot more than data quality engineers.” Bowers said, “It’s one of my favorite positions because it’s very inexpensive and yet incredibly valuable to the enterprise.”
Hiring data quality engineers to clear up data issues allows higher paid BI engineers, data scientists, and data engineers to focus on producing higher-level business-critical insights.
In a very small company, the database administrator (DBA) is often a “catch-all” position, he said, but in a large enterprise, the DBA position has become more specialized to the point where the DBA just operates, administers, or even develops on a database. Bowers noted that the salary for this position is only $80,000, “and that really bothers me . The skill level required — especially being an Oracle DBA — is so high, that it’s just insulting.” The devaluation of the DBA position reflects the state of the industry, he said, where positions that are perceived as adding more value to the business and positions that are hard to fill are paid more. DBAs are plentiful and compensated so little compared to what is required to become a DBA, he said.
Thirty years ago, DBAs made more than software engineers, but Bowers also sees a connection between this devaluation and the unwillingness of many DBAs to embrace automation and learn new skills. Those that push themselves and learn to master new skills become more valuable and can move up to a higher paying position, such as BI engineer, he said.
Data modelers work with data architects and DBA designers and developers to model data, translating business rules into usable conceptual, logical, and physical models and database designs. Good data modelers are highly valued by the enterprise and this is one situation where a simple change in title can increase salary — if the modeling skills are there, he said. “A lot of people think they model data well, and they don’t.” Data Modeling is an art, he said, and because it’s such a hard job to do well, modelers get paid well if they do it well.
Bowers had warnings for businesses looking to hire a data modeler. “Because everybody claims to be a good data modeler,” it’s important to interview and evaluate thoroughly to ensure that candidates have proven modeling skills. Bad data models make data integration very difficult, and apps based on flawed models can never perform properly, he said. “It’s a huge value add to get a good data modeler.”
Business Intelligence (BI) Engineer
DBAs with strong people skills might consider becoming a BI engineer. The average salary is $27,000 over the average DBA, reflecting both strong technical skills and business value. The primary difference is that the BI Engineer requires an outgoing, friendly personality, and the ability to translate the needs of people on the business side into user-friendly reports and dashboards.
The difference between a BI engineer and a data analyst is that the BI engineer has a more varied skillset, which includes machine learning, data visualization skills, and an ability to apply dimensional modeling successfully to meet business needs.
The data engineer does the same work as the BI engineer, but using big data, which results in an average salary increase of $10,000. Rather than working with on-premise technologies, Data engineers work with data lakes, cloud platforms, and data warehouses in the cloud. “More cutting edge technology makes you more money, even if you just perform the same function for your business,” he said.
Although the salary difference between data engineer and data scientist is minimal, the primary difference between them is that a data scientist needs a master’s degree at minimum, and preferably a PhD in statistics. Bowers noted that the time it takes to reach the same salary differs for both positions. With an advanced degree, the data scientist is likely to make $117,00 right out of school, whereas a data engineer might spend 10 to 20 years to get to that salary. “It’s a fun job if you like analyzing data and solving business problems it’s great, but you’d better love statistics, because that’s the foundation of all Data Science,” he said.
If the data scientist is on the fast track, the data architect is on the slow track. The data architect needs to have a comprehensive mastery of all the technologies that all other positions have, as well as the personality and skill to work successfully and gracefully with both IT and business people. “It takes a lot of experience to become a data architect. You can’t go to school and graduate and have this level of experience,” said Bowers.
How to Increase Your Salary
- The highest paid jobs require deepest knowledge of the widest variety of technologies, and principal data architect is the best example.
- Knowing statistics and machine learning skills increase salary.
- Technology skills increase salary the most, with the biggest increases seen by those in BI or Data Engineering.
- Learn cutting-edge skills. Specialized niche technologies are particularly well-paid.
- Twenty years ago, DBA was one of the highest paid technical jobs and now it is near the bottom. Staying current is a critical to competitive salaries, whatever the position.
- Work closer with IT Management. Develop relationships to maximize opportunities.
- Architects make more money because they have gained the trust of IT Management, and social skills can be learned — often the difference in upward mobility.
How Companies Can Succeed with Data Modeling
- You get what you pay for, so hire good data modelers and pay them well. Find an expert hiring firm with experience specifically hiring data modelers.
- A software engineer who is good at Data Modeling is more expensive but delivers the best results.
How Companies Can Succeed with Data Warehousing
- BI engineers using the Kimball style of dimensional modeling have a high rate of success.
- Senior BI engineers can easily train junior BI engineers to do the same work for less money.
How Companies Can Succeed with Big Data
- Data engineers can be distracted with new technologies rather than delivering business value. Hire the right data architect to direct and focus engineers.
- Data scientists using machine learning can deliver problematic results without proper knowledge and application of statistical principles and validation. Data scientists should be PhD-level statisticians, with a true understanding of proper research techniques and an ability to prove predictive models.
Which Positions Should You Hire?
- Data analysts are most cost-effective for one-off reports and finding some bad data. Hire right out of college and pair them with more experienced analysts as mentors.
- Data quality engineers are the lowest cost way to find and fix bad data in IT. Hire right out of college and pair with a more experienced mentor.
- BI engineers deliver maximum business value for automated analytics and dashboards. Hire right out of college.
- DBAs can focus on keeping databases running well, allowing data engineers, data scientists, and data architects to focus on more high-level priorities.
- Data engineers specialize in big data solutions, but technology and techniques are too new to provide guaranteed success. Ensure new hires are carefully vetted for skills and experience.
- Data scientists are cost effective when Data Quality is good, so hire less expensive data quality engineers to ensure scientists are freed from Data Quality tasks.
- Data architects are for large organizations that need vision across all data activities.
Check out Data Architecture Online at https://dataarchitectureonline.com/
Here is the video of the Data Architecture Online Presentation:
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