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What Does a Data Analytics Career Path Look Like in 2023 and Beyond?

“By 2025, smart workflows and seamless interactions among humans and machines will likely be as standard as the corporate balance sheet, and most employees will use data to optimize nearly every aspect of their work.” So write the wonks at McKinsey about our data-driven future, and they’re supported by recent movement in the data analytics market. According to Precedence Research, the data analytics market, in 2023 worth around $54 billion, will grow to nearly $350 billion by 2030 — about the size of the GDP of Denmark.

With so many businesses rushing to improve their data operations, a data analytics career path is not only increasingly viable for individuals from a variety of backgrounds, but also potentially quite lucrative. The US Bureau of Labor Statistics (BLS) foresees 23% growth in the data analyst job market over the next decade, an addition of 24,200 positions. This is much faster growth than the job market as a whole, for which the BLS foresees just 5% growth over the same time frame, and that’s before factoring in other data-centric roles like data scientist, data engineer, and data architect. What’s more, these positions offer above-average compensation: in 2021, the median pay for a data analyst was $82,430 according to the BLS, close to double the median annual wage in the US of $45,760.

If a booming job market, competitive salaries, and working with data to drive business growth appeal to you, you might be wondering how you can get started and, once you break into data analytics, what your career can look like. In this article, we break down a typical data analytics career path with real job postings. We’ll start by diving into what exactly data analytics is.

What is data analytics?

Data analytics is an interdisciplinary field focused on data collection, preparation, and analysis to extract insights and support decision-making across industries. Key skills for data analytics professionals include:

Data management: Data analytics professionals often need to be skilled in data collection methods like data mining for big data analytics, as well as methods of data storage and data manipulation in order to create efficient data pipelines and transform raw data into usable data, respectively.

Statistical analysis: When it comes to the actual analysis of data, data analysts make use of a host of statistical techniques like regression, optimization, and factor analysis to turn data into intelligence. For more on these techniques, check out our long-form explainer on data analytics.

Machine learning: While a professional working in data analytics works less with machine learning than a data scientist or machine learning engineer, in some cases they will utilize machine learning methods like deep learning for predictive analytics, which allows a business to determine the likelihood of future events, and prescriptive analytics, which suggests, or even automates, certain actions for a business. Again, see our long-form explainer for examples.  

Programming: Data analytics professionals rely on programming languages like Structured Query Language (SQL) and Excel to manage data and perform rudimentary analyses, respectively, and Python and R to perform advanced statistical analysis and, in some cases, write machine learning algorithms and train machine learning models.

Data visualization: Developing the insights is often just the first step: often, data analytics professionals need to communicate their findings to relevant stakeholders, which they do through data visualizations using tools like Tableau.

What’s a typical data analytics career path?

In sketching out a typical data analytics career path, we’ll focus on the most typical data analytics position: data analyst. At the end, we’ll touch on alternative roles, such as data scientist, data engineer, and data architect, that experienced data analysts can transition into.

  1. Bachelor’s Degree, Bootcamp, or Certificate Program

Most aspiring data analysts start their journey through a bachelor’s degree, bootcamp, or certificate program that will impart data analytics skills.

Data Analytics Bachelor’s Degrees

While specific bachelor’s degrees in data analytics do exist, students interested in ultimately going into the field often major in related fields like applied mathematics, computer science, or even economics, finance, or business if they want to focus on business analytics (read more about business analytics in our explainer). These majors often offer instruction in the skill areas outlined above, while distribution courses in the social sciences or the humanities can help students build crucial soft skills like storytelling and critical thinking.

Most bachelor’s degrees require a four-year commitment, but tuition varies widely. According to, the average undergraduate paying in-state tuition at a public university will pay a total of $102,828 over four years, while an undergraduate at a private university will pay $218,004.

If you’re interested in learning more about data analytics bachelor’s degrees, head to our guide to the best data analytics undergraduate colleges and universities.

Data Analytics Bootcamps

For someone who’s already earned a bachelor’s degree in another field or who simply doesn’t want to invest the time and money it takes to earn a four-year degree, data analytics bootcamps offer a path into data analytics measured in months, not years, with an average tuition of $11,727.

Data analytics bootcamps are geared towards providing attendees with the training needed to secure an entry-level position in the field. Accordingly, the emphasis is often on practical application rather than theory. For this reason, most bootcamps culminate with a capstone project of some kind, which lets students apply what they’ve learned in a real-world setting and begin building a data analytics portfolio to use for recruitment.

If you’re interested in learning more about these kinds of opportunities, head to our data analytics bootcamp guide.

Data Analytics Certificate Programs

An alternative to a data analytics bootcamp for someone who already possesses some of the skills necessary to work as a data analyst is a data analytics certificate program. These programs can often be completed more quickly, with some focused on developing specific skills instead of a comprehensive skill set. Other certificate programs do offer comprehensive instruction but provide less interaction and career support. In exchange, the cost of attending can be lower.

For more on the different kinds of data analytics certificate programs out there, head to our guide.

  1. Recruitment

At the same time as they complete their first course of data analytics study, aspiring data analysts will usually start their job search. Generally, the recruitment process will involve:

  • completing an internship to build experience and develop a relationship with ah company,

  • utilizing career-support services and staff provided by a university or bootcamp,

  • networking, first through friends, family, and alumni, and then building out,

  • researching data analytics roles in different industries,

  • building out a data analytics portfolio through school- and independent projects,

  • and optimizing a resume to highlight skills and experience.

For tips and recommendations for the data analytics job hunt, check out our step-by-step guide to landing a data analyst job.

  1. Junior Data Analyst

Aspiring data analysts will generally find their first success — and the first stop on their career path — as a junior data analyst. On average, junior data analysts make $57,266 according to While some companies ask that candidates bring with them a certain amount of experience in the field, these requirements can often be met through internships and education, especially if the candidate can demonstrate mastery of technical skills. Sometimes, employers will also ask that entry-level candidates have relevant industry experience, a requirement that can also be met through internships.

Junior Data Analyst – Eastern Mountain Sports


  • Support the analysis and decision making of senior managers.

  • Provide informative analysis.

  • Proactively identify trends in data and make recommendations on operational improvements.

  • Understand processes and systems and recommend changes to continuously improve them.

  • Support infrastructure, controls, and business intelligence.

  • Create, troubleshoot, and validate management reports.


  • Recent college degree, or equivalent education in the areas such as Business, Finance, Mathematics or Economics (relevant work experience will be considered) 

  • Strong problem-solving acumen

  • Ability to communicate with wide range of stakeholders with various levels of technical understanding

  • Able to demonstrate exceptional numerical and analytical skills with outstanding attention to detail

  • Skilled at analyzing large and complex datasets

  • Understanding of Microsoft Excel formulas/pivot tables

  • Advanced Microsoft Excel skills

  • Knowledge of business intelligence tools like Power BI, Tableau, and Looker

  • SQL and/or other programming knowledge

  • Experience in a retail setting

  1. Data Analytics Master’s Degree

A bachelor’s degree, bootcamp, or data analytics certificate can help you land a junior data analyst role and may be enough to gain you a promotion to a mid-level role, but frequently those interested in moving faster down a data analytics career path will pursue a master’s degree to level-up their skills and get a handle on the latest field-leading techniques and practices.

Master’s degrees are also a go-to for those who already hold bachelor’s degrees in a different STEM field — or even the social sciences or humanities — who want to gain data analytics-specific skills to make a career transition. In many cases, a master’s degree will allow individuals with these profiles to enter the field through a more senior position.

In past years, master’s instruction has increasingly been moving online. In 2016, according to Urban Institute, master’s students studying online made up nearly a third of all master’s students, up from just 5% in 2000 and 21% in 2012. The successes of collegiate learning during the coronavirus pandemic have likely further convinced prospective graduate students of the efficacy of online study, though it is still too soon to see concrete data attesting to the fact.

The proliferation of quality online learning platforms and efforts of online-focused curriculum designers have no doubt played a role in the rise of online graduate programs, but for early-career professionals looking for a leg up, online study also poses the distinct advantage of flexibility. Online master’s programs frequently offer part-time and/or asynchronous options that can allow learners to continue working or taking care of familial obligations while they study, which ultimately can lower the total cost of education.

If you’re interested in online data analytics master’s opportunities, you can find more information and our program recommendations in our guide. Our general guide to data analytics master’s degrees has recommendations for in-person study as well.

  1. Mid-Level Data Analyst

As we mentioned above, a master’s degree can give an early-career data analyst a leg up when seeking a mid-level position — but just what do mid-level data analysts do? While the answer (of course) varies, mid-level data analysts are typically expected to draw on an enhanced skillset and experience to perform more advanced analysis and take more of a leading role in the analytics process, owning analytics initiatives from start to finish. 

Because they often serve in some sort of leadership capacity, companies often want candidates for mid-level data analyst positions to have an aptitude for leadership or experience leading small teams. Mid-level analysts will also typically draw on specific industry or capability experience they developed earlier in their careers. Sometimes, these specializations will be advertised in job postings through particular titles, such as “financial analyst,” “market research analyst,” or “marketing analyst,” to name a few. In exchange for their higher qualifications and experience, mid-level data analysts receive higher compensation, though exact salaries can vary widely.

Debt Capital Markets Data Analyst – Bloomberg


  • Use engineering skills and data science knowledge to develop and support the data pipelines and processes that we build for our clients and firm-wide partners.

  • Use data analysis skills to find opportunities to build automated solutions for data standardization, ETL pipelines, and human-in-the-loop data processing.

  • Design, develop, and deploy data quality validations to drive quality improvements.

  • Use statistical knowledge and data visualization to report on results of ongoing operations and projects.

  • Partner with a wide variety of collaborators, including Engineering, Product and Sales, to drive and lead product development and execution.

  • Stay on top of the latest developments in your field and suggest improvements to supported workflows.


  • Bachelor's/Master's degree in STEM-related field or equivalent professional working experience

  • Preferable experience in Data Science/ML, Computational Finance, Statistical modeling

  • Python programming experience addressing business problems and working on big data sets

  • Proficiency with Python, git, UNIX, software engineering principals, SDLC, MDLC

  • Proven track record of using technology to innovate data products and processes

  • Strong passion for data, technology and finance with the aspiration to work in the financial/FinTech services industry

  • Experience using data analysis and visualization tools to complement our research and approach

  • Problem-solving mentality and attention to detail

  • Financial Market experience, including an understanding of how benchmarks are used by our clients (preferred)

  1. Senior Data Analyst

Senior data analysts are typically expected to have 5 to 10 years of experience as a data analyst, often in a particular industry, and be able to perform more advanced analyses. Compared with the mid-level data analyst, senior data analysts have even more responsibility, overseeing multiple projects, mentoring early-career analysts, and leading initiatives to improve data pipelines. Senior data analysts often also answer to various upper-level managers and executives on the progress and findings of their projects. According to, the average salary for a senior data analyst is $100,104.

Senior Data Analyst, Operations Data – Virgin Galactic


  • Work closely with the engineering team to strategize for and execute the development of data products.

  • Collaborate with software developers and engineers who are skilled in machine learning to implement analytical models into production.

  • Recognize relevant data sources and sets for clients’ business needs.

  • Develop and use algorithms and models to mine large data stores.

  • Communicate analytical solutions to implement improvements to operational systems as necessary.

  • Communicating key information to IT and business leaders.

  • Use data mining state-of-the-art methods for solving problems.

  • Improve data collection to include relevant information for building analytic systems,

  • Optimize processes for data intake, validation, mining and engineering as well as modeling, visualization and communication deliverables. 

  • Automate data collection by building tools.

  • Create reports and presentations for decision support for the business.

  • Utilize data science techniques such as statistical modeling, machine learning, and artificial intelligence.


  • 8+ years of experience in an analytical role with a minimum of 5 + years of experience in managing demand and delivery of data analytics

  • Bachelor’s degree in Business, Finance, Economics, Mathematics or Accounting. MBA is highly preferred

  • Extensive experience in data architecture, mining and data manipulation to create customized reports  

  • Extensive Excel experience with the ability to design and maintain financial models and templates

  • Budgeting and financial planning experience, preferably with experience in operations, commercial airline and/or transportation industries

  • AI and Machine Learning experience (preferred)

  • Experience with Oracle, Anaplan, Power BI, Ultramain (preferred)

7. Data Scientist, Data Engineer, or Data Architect

After reaching the position of senior data analyst, data analytics professionals often leverage the skills they’ve learned in machine learning and data pipeline management to move into data science, data engineering, or data architecture, often with a significant pay rise.

Data Scientist

Like data analysts, data scientists are responsible for collecting, preparing, and analyzing data to extract insights, but data scientists also utilize advanced machine learning skills to develop new tools, techniques, and processes for doing so. The average data scientist in the US makes $139,631 according to

Data Architect

A data architect is responsible for designing more efficient pathways, protocols, and systems for data collection, preparation, storage, and analysis. The average data architect in the US makes $124,711 according to

Data Engineer

A data engineer takes up where a data architect leaves off: they implement more efficient data pipelines according to a business’ particular needs, resources, and goals. The average data engineer in the US makes $112,555 according to

For more on these positions, check out our articles on jobs in data analytics and the differences between a data analyst vs a data scientist.

What's next?

In this article, we’ve laid out what you can expect from a typical data analytics career path — with some real-world jobs as examples — plus previewed some more advanced roles that experienced data analysts can transition into. If this career path interests you, you can head to the articles we’ve linked throughout to learn more. For your convenience, we’ve also provided these links below.