Note: This is part 2 in a series. See part one at Roadmap for Your Data Career
“Where do you see yourself in 10 years?”
Does that question fill your brain with anxiety or confusion? There are hundreds of variables that could affect where we end up on the career landscape, some within our control and some without. But the better we understand the context and requirements for each role, the better we can begin to plan and take advantage of the right opportunities when they arise.
Career Landscape
The following map shows the data career landscape, with roles grouped into classes.
Data Career Landscape
The classes are organized according to similar job activities and deliverables. Individual contributor roles are at the top, with management and executive roles towards the bottom. Then each role is color coded according to a track, depending on the level of business or technical focus involved. Some classes, such as Analyze, consist of multiple color tracks, while others like Sell or Configure are focused on a single color. The roles in the center are involved with coordination and planning for multiple classes. The roles are generally organized with lowest seniority on top and increasing seniority toward the bottom, but each role may have many levels of seniority at some organization, such as Data Engineer Level 1 to Data Engineer Level 5. And each of these roles may vary depending on the organization.
As you locate the role where you are currently assigned, you can start to evaluate your location and the neighboring roles. A Business Analyst, for example, may be creating presentations and using software to gather data. The Research Analyst and Business Intelligence (BI) Analyst roles are within the next steps as the analyst gains more business or technical skills. Transitioning to a new role anywhere on the landscape is theoretically possible, but the most common steps will be within one or two steps or in an adjacent class.
The “Configure” job class may not be familiar to many people who are new to the field. The roles might have names like “Salesforce Developer”, “SAP ABAP Developer, or “NetSuite Configuration Consultant”. In the Enterprise IT world, these roles are common and well-paying. But in college programs these roles are rarely mentioned because they involve specific software packages and are constantly evolving. Platforms like Salesforce allow companies to build new applications with a combination of low-code and full-code environments. This job class is constantly hiring at consulting firms and vendor implementation groups.
Many of the most interesting data roles span more than one class. Data Scientist, for example, is a blend of analysis and developer skills. Dev/ML Ops will blend software configuration and support roles. These cross-class roles also tend to be the most sought-after and highest paid due to the multiple skillsets involved. Many people claim to be truly cross-functional unicorns, but not everyone can perform the wide variety of skills involved.
Career Migration
The following image shows typical migration paths between classes of roles.
Data Career Migration
These migration paths are where typical career advancement might occur but are not intended to be exhaustive. The general movement pattern is from top to bottom as you gain seniority and may perhaps feel burned out from previous roles. These migration patterns are important to recognize because they may explain why you are not getting responses to job applications! An experienced Project Manager, for example, may not get many responses to a job application as a Salesforce Developer (Software Configuration) because it is a rare combination. But a Software Engineer transitioning to a Systems Architect role will be a natural step and will surprise no one.
A crucial question in the migration patterns is: what happens when you reach a Sales, Management or Executive role and decide you want to go back? There are few arrows going in the opposite direction. These roles lower on the chart tend to have less technical deliverables and may cause skills to atrophy. And the pay is usually lower if you go back. Three great options for people who may be tired of Managing or Selling are to do consulting, teaching or start a company. While there may be a scarcity of executive roles in large companies, there is an unlimited supply at the next batch of startup companies!
Picking a Track
If you are just getting started, take a look at the roadmap that I published in a previous post. As you look through the roadmap, you can decide which type of track appeals to you most. A technical track can easily transition to hybrid, but a business track is not so easy to transition due to the coding skills required.
Picking a Career Track
Conclusion
Don’t worry if you don’t know what you want to do in 10 years. Very few of us do. You can focus on evaluating where you are on the data career landscape, and identifying roles that look appealing for the future. If you don’t recognize any of the roles on the map, take some time to learn about them and you might find some interesting ideas. If you see an attractive role that is not within your reach, then you can go back to school to make the transition. The good news is that there is an abundance of opportunities in the data field and the industry is growing faster each year.
Stan Pugsley is a freelance data engineering and analytics consultant based in Salt Lake City, UT. He is also a lecturer at the University of Utah Eccles School of Business. You can reach the author via email.
