Data management roles: Data architect vs. data engineer, others
As more organizations become aware of the important role data plays in their overall business practices, there’s more demand for skilled workers to handle various data management roles. But there’s also more confusion around the differences between positions like data architect, data modeler and data engineer, and which ones are most valuable to an organization.
Michael Bowers, chief architect at NoSQL database vendor FairCom Corp., sought to cut through some of that confusion during a session at Dataversity’s inaugural Data Architecture Online event. The online conference covered key strategies and technologies needed in order to build and manage a modern data architecture.
In his session, Bowers, who has more than 30 years of experience as a data professional, compared eight data management and analytics jobs in particular, including their key functions, their salaries and the technical skills they require. He also offered advice on how to increase one’s salary in the data management field and how organizations should go about building a data management team.
The job descriptions and salaries, which are averages from across the U.S., come from job sites glassdoor.com, indeed.com and payscale.com, as well as Bowers’ own work experience. He noted that he has held, managed or led projects involving all of the data management roles he discussed except for data scientist.
The positions bringing more value to the business — like data architects, data modelers and data scientists — are harder to fill, Bowers said. That’s because people in those positions generally need to be more familiar with cutting-edge technology than other data management workers are. How do these data management roles compare? Data architects design and help implement database systems and other repositories for corporate data, Bowers said. They’re also responsible for ensuring that organizations comply with internal and external regulations on data, and for evaluating and recommending new technologies, he added. Bowers described a data architect as a “know-it-all” who has to be familiar with different databases and other data management tools, as well as use cases, technology costs and limitations, and industry trends. “I had to master a ton of technologies to become a data architect,” he said. A data modeler identifies business rules and entities in data sets and designs data models for databases and other systems to help reduce data redundancy and improve data integration, according to Bowers. Data modelers make less money on average than many other IT workers, but you get what you pay for, he cautioned. “It’s hard to find a good data modeler,” Bowers said. “It’s an art. Anyone who says you can just throw data into a computer and get a good model out automatically is wrong.” Software engineers who are also good at data modeling can deliver the best results, he added, though it typically is more expensive to hire them.
A data engineer is essentially a BI engineer for big data, Bowers said. Data engineers build data pipelines connecting databases and big data systems, and like data architects, they must understand the intricacies of various cloud and on-premises technologies. But in dealing with big data, data engineers can often be distracted by those technologies rather than focus on delivering business value, Bowers said. “They just spend all their time playing and not getting [things] done.” Meanwhile, data scientists — who find, prepare and analyze data using machine learning algorithms and other advanced analytics applications — can deliver problematic results without proper knowledge and application of statistical principles, which Bowers described as the foundation of data science. Developing, testing and validating predictive models “really does take a Ph.D.-level statistician,” he said.
