A data analyst prepares and analyzes data to help companies and organizations solve complex challenges and make better decisions. If you’re interested in data analytics, this guide covers everything you need to know about the data analyst career path, including job titles at every level, typical salaries, and certification options.
Data Analyst Career Path
There’s no shortage of opportunities to pursue in data analysis, which is as true at the entry level as it is at the senior level.
“Unlike many IT roles, data analysts interact and coordinate with several enterprise teams, such as product management, data engineering, data science, IT, sales, marketing, finance, etc.,” says Aswini Thota, a lead data scientist at Bose Corporation with over 12 years of data analytics experience. “These diverse interactions provide numerous career tracks for data analysts.”
For example, data analysts can learn advanced machine learning methods to help transition to a data scientist role. Or, they can learn management principles to transition to program or product management roles.
The career path for an entry-level data analyst is highly dependent on the individual’s learning appetite, specific goals, and desired impact.
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Skills for Data Analysts
The data analyst career path involves a wide range of tasks and skills, such as:
- Using the right tools and resources to optimize data collection
- Blending data assets to reveal a complete story
- Translating business problems into measurable hypotheses
According to Thota, a trained data analyst is good at quantifying uncertainty and spotting upcoming opportunities. He explains that these critical skills help people progress in a data analyst career path — either in the same data vertical or in a leadership role.
“Data analysts typically develop experience in a broad spectrum of skills, which allows them to specialize in a technical area of expertise, change career paths, or move into leadership roles,” Thota says.
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Thota adds that senior-level employees must have considerable technical leadership experience in data analytics to progress to the leadership level. This involves a demonstrated ability to manage complex projects and lead large teams before applying for advanced leadership-level data analytics roles.
“To move into a leadership role, senior or principal data analysts should look for opportunities to manage impactful projects requiring collaboration, stakeholder management, and decision-making skills,” Thota notes. “It’s also crucial to develop people management skills, which can be learned without necessarily having a team reporting to you.”
As an example, leading an agile squad can provide valuable leadership skills and experience. Data analysts in senior-level roles should also focus on developing the skills necessary to create a vision and strategy for using data to drive business outcomes.
Data Analyst Career Path Trajectory
If you’re wondering how to become a data analyst, you should know that the career path doesn’t always move in a straight line. While most entry-level data analyst roles have some commonalities, senior-level roles can vary.
“You start off usually as a junior data analyst, and may evolve into a senior data scientist, or principal data scientist after having solved complex challenges, won some projects and funded awards, and also helped to recruit a team,” says Chris Mattmann, chief technology and innovation officer at NASA Jet Propulsion Laboratory, and an adjunct research professor who teaches data science and big data at the University of Southern California.
“If you can show that with more collaborations and data that you can solve increasingly high business value challenges with data — and perhaps have a few patents, or invented a few new technologies or techniques in open source — you can grow in the field.” Mattmann adds that senior or principal data scientists may eventually evolve into the C-level chief data officer position.
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“With time, the junior analyst can reach a senior or managerial level, leading teams and owning the data processing, or even turn to specialize in a certain field — healthcare, finance, marketing, etc. — now that they have the foundational training and experience,” says Theresa Kushner, who has more than 25 years of experience in data analytics and is head of the North American Innovation Center at NTT Data Services.
“Some advance beyond the role of the data analyst into data scientists, using data to optimize the business performance and predict future outcomes,” she continues.
Entry-Level Data Analyst Tasks
When launching your career as a junior or entry-level data analyst, you’ll have a hands-on role in data collection, management, and analysis, says Kushner.
The first task of an entry-level analyst is to get intimately familiar with the data you’ll be using in your analysis.
Kushner explains that this means understanding what data is available to you for analysis, the quality of that data, and how and when it is updated or corrected. “For example, if you are a financial junior analyst, this may mean that you must become familiar with the information in your ERP system,” she says.
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Senior Data Analyst Tasks
This data familiarity allows you to advance to a senior analyst position by learning to relate information from your area of expertise to others. Kushner notes that if you’re a financial analyst, you might use your knowledge of the transaction created in the ERP system to help marketing understand customers’ buying cycles.
In addition, a crucial part of the data analyst career path trajectory involves advancing further in your analysis. This enables you to begin to apply new techniques to the data you understand. “For example, the financial analyst at more advanced levels should be able to predict or forecast expenditures or revenues using statistical techniques, such as linear regression,” Kushner says.
The progression from junior to senior analyst is also highly dependent on the tasks of the analytic organization. “Roles may vary depending on the maturity of the organization,” Kushner explains. “Those just beginning to build a data team will find that they may spend more time analyzing data than actually using it for predictions or optimization.”
According to Kushner, most job openings within companies forming their data organizations are for senior- or advanced-level analysts. “This is because data organizations that are new or forming need capabilities fast. They may not have time to grow their own analysts,” she says. “The requirement for junior analysts is more often found in mature data organizations who are providing training and career paths for junior analysts. However, even in these organizations, you still find the demand for senior analysts to provide advanced analytics and AI — much sought after skills.”
Careers in Data Analytics
Each level of the data analyst career path has several potential job titles. Below, Thota shares his expertise in identifying data analyst roles, salary bands, feeder roles, and the next roles often attained. The average salary ranges reflect base pay only.
Entry-Level Data Analyst Roles
- Common job titles: Junior data analyst, data analyst I, business intelligence developer, associate data analyst
- Average base salary: $65,000 to $90,000
- Feeder roles: Intern data analyst, co-op data analyst, systems analyst, SQL developer
- Next role: Senior data analyst, manager of data analytics, lead data analyst
Mid-Level Data Analyst Roles
- Common job titles: Senior data analyst, manager of data analytics, senior manager of data analytics, senior business intelligence developer, data analyst IV
- Average base salary: $95,000 to $160,000
- Feeder roles: Data analyst, business intelligence developer, associate data analyst
- Next role: Senior product manager, director of analytics, director of customer insights, director of experimentation, director of data governance, staff data analyst, squad/train lead, agile product owner
Advanced-Level Data Analyst Roles
- Common job titles: Senior product manager, director of analytics, director of customer insights, director of experimentation, director of data governance, staff data analyst, squad/train lead, agile product owner, data architect, data scientist
- Average base salary: $130,000 to $200,000+
- Feeder roles: Senior data analyst, principal data analyst, manager of data analytics, senior manager of data analytics, senior business intelligence developer, data analyst IV
- Next role: Vice president of data analytics and insights, head of data, vice president product manager, vice president of advanced analytics, senior vice president of data analytics, distinguished fellow, chief data officer
Glassdoor provides similar average annual salaries for each level of the data analyst career path:
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Data Analyst Education and Certifications
Most universities with STEM programs educate students on the theoretical and research aspects of data analysis and data mining. Getting a bachelor’s degree in computer science or business analytics can give you the skills you need to get into the field.
However, college programs may not provide hands-on experience with real-world data analysis. Thota suggests college graduates can fill this gap by investing in online courses and certifications.
“Massive open online course (MOOC) offer a wide range of courses in data analysis and data mining,” Thota says. “MOOCs are especially useful if you want to specialize in a specific sub-topic, such as regression analysis, cloud computing, or data visualization.”
Certifications
Another popular way for college graduates to demonstrate their skills in data analysis to potential employers is via certifications. Although many major universities offer certification for data analysts, several other types of certifications are also available. “The selection depends on the tools or technology stack you are most comfortable with,” Thota says.
If you want to be a generalist and focus more on generating insights, some popular routes to certification include:
- Certified Analytics Professional (CAP)
- Certified Business Intelligence Professional (CBIP)
- Associate Certified Analytics Professional (aCAP)
However, if you want to focus on building applications, you can pursue a vendor-specific certification through tech companies. Examples include Cloudera, IBM, EMC, SAS, Microsoft, Tableau, and MongoDB.
Examples of vendor-specific certifications include Tableau’s Desktop Certified Associate and Microsoft’s Power BI Data Analyst Associate. “If you are familiar with a data analysis tool and see yourself building analytical applications using that product, then a vendor-specific certification is the way to go,” Thota says.
You can also learn more about data analysis tools with specific certifications. “In this area, Snowflake, Data Bricks, and Dataiku also offer programs for improving the skills of data scientists,” Kushner says.
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