A data analyst career can be highly rewarding, with lots of specialized opportunities in a variety of industries. However, it also takes time to establish yourself in the analytics field. If you’re just starting out as a data analyst, what factors do you need to consider? What kinds of career paths can you take?
After You’ve Learned the Basics
At the most basic level, data analysts use programming languages like SQL, Python, and R, as well as data visualization software and statistical analysis, to analyze data for their organizations. Key skills include (but certainly aren’t limited to):
- Data Analysis
- SQL
- Python
- Tableau
- Microsoft Power BI
- Data Science
- Data Visualization
- Business Intelligence
- Data Warehousing
- Extraction Transformation and Loading (ETL)
After you have the required skillset and land your first job, you’re far from done. All jobs in technology require lifelong learning to keep up with evolving technology. Data analysis is no different, so plan to keep learning. Take classes, read books and articles, attend seminars, and practice new technologies as they come out. Nobody becomes an expert quickly; plan to practice throughout your career.
Once you land your first job as a data analyst, you’ll typically start at a junior level. Here are the three main levels at a typical organization:
Junior Data Analyst: At this level, you’ve acquired the basic skills to get into the job, but you still have much learning to do. You’ll likely be given assignments to analyze data and put together reports in different formats; but you may need assistance and mentoring from the mid-level and senior data analysts.
Mid-level Data Analyst: Sometimes simply called “data analyst,” this is the level where you’ve mastered the core aspects of the job. You’ll be a productive team member analyzing the data, and occasionally you’ll be asked for input from team leads, such as how to solve a problem or what technologies to use.
Senior Data Analyst: When you reach this level, you’ll likely get a nice pay raise. You may also be asked to mentor the junior level analysts more. You’ll have more guidance and control over how the organization analyzes its data.
In most technology careers, the move from mid-level to senior doesn’t always involve a huge change in work scope, unless you’re asked to lead a team—but it can result in more money. As you think about your long-term career, you need to consider what you might want to do beyond senior data analyst.
Independent Consulting
For example, you could become an independent consultant who goes from company to company, sharing your expertise and helping them with the data problems.
To succeed as an independent consultant, you need a broad range of skills, with a particular mastery of one or two. For example, you might develop expertise in financial data analysis or perhaps healthcare data analysis. The more specialized you become, the higher rates you can command.
It might seem like you’re limiting your opportunities by specializing, but typically the opposite is true. As you gain ground in a certain industry, you will network and meet other people and organizations that need your services. Independent consultants typically charge a premium—but you also have to spend time marketing your skills.
Company Management
In this path, you ultimately leave the daily work of doing data analysis to focus on business management. To reach this path, however, you might need some extra schooling; for example, a Master’s of Business Administration (MBA) would be a great help. With your technical knowledge, you would have a great understanding of what the company’s teams are doing and how to manage them effectively.
Project Management
As a technical lead, you might step away from data analysis to become a Scrum master, program manager, or project owner, focusing in a holistic way on the organization’s workflows. This requires learning project management, Scrum, and Agile.
Focusing on Analysis
Many people in tech don’t want to move up the ladder. If you reach the senior data analyst level and simply want to stay, that’s great. But keep in mind that your skillset will still need to evolve over time as technology grows, especially if you’re in this for the long haul. Plan to keep studying!
Branching into Other Directions
Data Analysts have a big overlap with most other data-oriented careers, such as data scientist or data engineer. If you’re interested in moving to these areas, you’ll want to sharpen your skills needed for those roles. For example, data scientists use a good deal of mathematical knowledge in their roles, and their analysis is often much more strategic. If you’re interested in this path, you’ll likely need to enroll in courses geared specifically for the move from data analyst to data scientist.
Meanwhile, data engineers often work more “behind the scenes” managing the database systems. Those who want to migrate to a career as a data engineer will need to learn about maintaining data systems, including relational database systems, and even cloud database management. Data engineers might spend time optimizing their data queries to run in parallel on a multiprocessor cloud system, which requires a deeper knowledge of how data is stored and managed. Some data engineers also need to do more programming, such as in R, Python or even C++. And again, there are courses and books that can teach you this information.
Conclusion
Data analytics is a great career with a lot of different paths. Start planning early and prepare to learn for life. Don’t be afraid to branch out in different directions from what you originally planned. The important part is to keep perfecting your skillset, learning new technologies, and moving forward.
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