The Top 10 Data Analytics Careers For 2025: Skills, Salaries & Career Prospects
If you’re considering a career in data analytics, you may already know that roles such as data scientist, information security analyst, and operations research analysts, all feature in the top 10 fastest-growing occupations in the US. Yet there are many different roles to consider when it comes to careers in analytics.
Here, we outline 10 of the top careers related to analytics, exploring the role and responsibilities, required skills, average salary, and career prospects. We've also collated some courses to learn data analysis to help you get started.
Why Choose a Career in Data Analytics?
If you’re reading this article, you’re likely already curious about analytics careers. You may have heard about the high earning potential and high demand of these roles. While these are certainly both true, there are other reasons to consider a job in this field, including:
- Variety of roles. There are many different types of roles within the field of analytics, including data scientists, business analysts, and data engineers, which means that there is likely a role that aligns with your interests and skills.
- Use of technology. Analytics professionals use cutting-edge technology and tools to extract insights from data, which can be exciting and challenging.
- Impactful work. Professionals in this industry can play a crucial role in helping organizations make data-driven decisions, which can have a significant impact on the success of the organization.
- Career advancement. As you progress in this industry, you’re likely to find opportunities for career advancement, with many professionals starting in entry-level roles and working their way up to management positions.
- Job security. As data becomes an increasingly important asset for organizations, the demand for analytics professionals is likely to continue to grow, providing job security in the field.
Other reasons to work in an analytics job include the flexibility modern organizations offer, the continuous learning on offer, and the opportunity to work with like-minded professionals.
The Top 10 Data Analytics Careers
As we’ve touched on already, there are quite a few roles that utilize analytics in their day-to-day work. We’ve picked out ten different analytics jobs that you might want to consider.
1. Data Scientist
If you’re curious about how to become a data scientist, we’ve got a full article covering the steps you need to take. Essentially though, this role is about working to deeply understand and analyze data to provide actionable insights.
Job Responsibilities:
- Collecting, analyzing and interpreting large sets of complex data
- Using statistical and machine learning techniques to identify patterns and trends in data
- Making predictions and informing business decisions
Required Skills:
- Background in computer science, statistics, or mathematics
- Proficient in programming languages such as Python and R
Average Salary:
- According to our data scientist salary article, the average for a role in the US is $123,069 in 2024, although this depends on the exact location, role, organization, and individual.
Career Prospects:
- Strong, with the Bureau of Labor Statistics projecting a 36% growth in employment in this field between 2023 and 2033.
Course to start with:
- Our Data Scientist with Python and Data Scientist with R tracks give you the career-building skills you need to succeed as a data scientist.
2. Business Intelligence Analyst
In brief, a career in business intelligence analytics focuses on helping organizations make data-driven decisions by analyzing and interpreting complex data. In this role, you’ll use a variety of tools and techniques to extract insights from data and present this information in a clear and actionable format to decision-makers.
Job Responsibilities:
- Collecting and analyzing data to help organizations make better business decisions
- Using data visualization software to present data in a way that is easy for non-technical stakeholders to understand
Required Skills:
- Background in business or a related field
- Experience with data warehousing and BI tools such as Power BI, as well as SQL, Excel, and Tableau
Average Salary:
- According to data from Glassdoor, the average base pay for a business intelligence analyst in the US is $101,000 per year in 2024.
Career Prospects:
- Strong, with the Bureau of Labor Statistics (BLS) projecting an 11% growth in employment in this field between 2023 and 2033.
Course to start with:
- Our Introduction to Power BI course teaches you how to use the data visualization software to build impactful reports.
3. Data Engineer
If you want full details on how to become a data engineer, how to learn data engineering, or want to learn more about what a data engineer does, we have separate resources dedicated to the topic. This particular analytics career is one of the most technical profiles in the field of data science, bridging the gap between software and application developers and traditional data science positions.
Job Responsibilities:
- Designing and building the infrastructure and systems that support data collection, storage, and analysis
- Managing and maintaining large data sets and databases
- Ensuring data is accurate, accessible, and secure
Required Skills:
- Strong programming skills in languages such as Python, Java, and SQL
- Experience with big data technologies such as Hadoop and Spark
Average Salary:
- As we explore in our article about data engineer salaries, the average for this role in the US is around $127,315 per year going into 2025.
Career Prospects:
- Strong, as more and more organizations rely on data to inform their decision-making. LinkedIn highlighted that engineers are in high demand still, with data engineers being top of the pile.
Course to start with:
- With the Data Engineer with Python career track, you can kick-start your journey to becoming a data engineer and learn the essential skills.
4. Business Analyst
You may have noticed that we’ve already mentioned the business intelligence analyst role above. However, this role is slightly different. We covered how to become a business analyst in one of our webinars, which shows why careers in business analytics are so popular. You can also find out more about business analyst certification in a separate article.
Essentially, while both roles focus on collecting, analyzing, and visualizing data, identifying pain points, and generating reports, business intelligence use mainly descriptive analytics, while business analytics uses diagnostic, predictive, and prescriptive analytics. You can learn about the types of analytics in a separate post.
Job Responsibilities:
- Identifying and analyzing business problems and opportunities
- Developing solutions to improve business processes and performance
- Communicating with stakeholders to gather requirements and provide recommendations
Required Skills:
- Strong analytical and problem-solving skills
- Excellent communication and presentation skills
- Experience with project management
- SQL, Excel, and Tableau
Average Salary:
- According to our exploration of business analyst salaries, the average salary for a BA is $84,778 per year.
Career Prospects:
- Strong; business analysts are in high demand across a variety of industries, and the BLS predicts a 25% growth by 2030.
Course to start with:
- Get started with our SQL for Business Analysts course to boost your skills and learn to make data-driven decisions.
5. Marketing Analytics Manager
There are various ways to use data science in marketing. A marketing analytics manager brings many of these functions together. They are responsible for using data and analytics to inform and optimize marketing strategies. It’s a career in data focused on collecting and analyzing data on areas like consumer behavior, marketing campaign performance, and market trends to gain insights that inform and improve marketing efforts.
Job Responsibilities:
- Collecting and analyzing data to inform marketing strategies
- Using data to improve the effectiveness of marketing campaigns
- Communicating with stakeholders to gather requirements and provide recommendations
Required Skills:
- Strong analytical and problem-solving skills
- Experience with data visualization and marketing analytics tools
- Strong communication and presentation skills
- SQL, Excel, Python, and R
Average Salary:
- According to Glassdoor, marketing analytics managers can expect a salary of around $112,000 per year in the US.
Career Prospects:
- We couldn’t find any exact figures on marketing analytics manager roles, but market research analysts, a close comparison, are expected to grow 8% between 2023 and 2033.
Course to start with:
- With our Marketing Analytics with Python skill track, you’ll learn how to analyze campaign performance, measure customer engagement, and predict customer churn.
6. Financial Analyst
In a separate post, we explored how financial analysts can leverage their data skills. It’s a role that requires heavily quantitative and data-oriented skills, analyzing data, communicating insights, creating forecasts, and predicting outcomes.
Job Responsibilities:
- Collecting and analyzing financial data to inform business decisions
- Identifying trends and patterns in financial data
- Developing financial models and forecasts
Required Skills:
- Strong analytical and problem-solving skills
- Experience with financial modeling and analysis
- Strong communication and presentation skills
- SQL, Excel, R, and Python
Average Salary:
- According to Glassdoor data, the average financial analyst salary is $79,000 per year.
Career Prospects:
- Strong, the BLS projects employment growth for financial and investment analysts of 9% from 2021 to 2031.
Course to start with:
- Our Finance Fundamentals in Python skill track is the ideal place to learn about making data-driven financial decisions.
7. Quantitative Analyst
We’ve created a whole guide on how to become a quantitative analyst, an analytics career focused on using mathematical and statistical techniques to help organizations make financial decisions. As a quant, you may work in areas such as in risk management, portfolio management, and trading.
Job Responsibilities:
- Using mathematical and statistical techniques to model financial and economic systems
- Identifying patterns and trends in data
- Developing and implementing quantitative investment strategies
Required Skills:
- Strong analytical and problem-solving skills
- Background in mathematical or quantitative fields such as engineering, physics or mathematics
- Experience with programming languages such as Python and R
Average Salary:
- Data from Glassdoor shows that the average salary for a quantitative analyst is $147,000.
Career Prospects:
- Strong, as quantitative analysts are in high demand in the finance industry. The field of financial analysis, which includes quantitative analysis, is projected to grow by at least 8% between 2022 and 2032.
Course to start with:
- Get started with our Quantitative Analyst with R career track and cover all of the essential areas to start your career in analytics.
8. Risk Analyst
Risk analysts help organizations identify, assess, and prioritize potential risks that could impact their business operations. They use a variety of tools and techniques to evaluate the likelihood and potential impact of different risks and then develop strategies to mitigate or manage those risks. As far as careers in analytics go, this is up there among the most interesting.
Job Responsibilities:
- Identifying and assessing potential risks to an organization
- Developing and implementing risk management strategies
- Communicating with stakeholders to gather requirements and provide recommendations
Required Skills:
- Strong analytical and problem-solving skills
- Experience with risk management and analysis
- Strong communication and presentation skills
- Knowledge of Python, R and SQL
Average Salary:
- The average annual salary for a risk analyst is $85,000 according to Glassdoor.
Career Prospects:
- Strong; one report from 2024, the risk management market will grow from a worth of $213.6 billion in 2023 to $44.1 billion by 2032.
Course to start with:
- Either our Quantitative Risk Management in Python or Quantitative Risk Management in R courses are a great place to start, depending on which language you want to learn first.
9. Data Governance Analyst
As a relatively new career in data, these professionals help organizations ensure that their data is accurate, consistent, and compliant with legal and regulatory requirements. They work to establish policies, procedures, and standards for data management and ensure that these are being followed throughout the organization.
Job Responsibilities:
- Developing and implementing data governance policies and procedures
- Ensuring data is accurate, accessible, and secure
- Communicating with stakeholders to gather requirements and provide recommendations
Required Skills:
- Strong analytical and problem-solving skills
- Experience with data governance and data management
- Strong communication and presentation skills
- Knowledge of SQL, Python, and Java
Average Salary:
- Salary data from Glassdoor shows that the average pay per year for data governance analysts is $113,000.
Career Prospects:
- Strong, as data governance becomes increasingly important with the growing amount of data being collected and stored. Data suggests that the data governance market was worth $3.96 billion in 2024 and is forecast to grow to $13.92 billion by 2031.
Course to start with:
- Start with the basics of Data Governance Concepts with our introductory course.
10. Data Visualization Engineer
Another relatively new career is that of data visualization engineer. These professionals are responsible for designing, developing, and maintaining data visualization systems and dashboards. They use a variety of tools and technologies to create interactive and visually appealing representations of data that enable users to quickly and easily understand complex data sets.
Job Responsibilities:
- Designing and developing interactive data visualizations
- Using data visualization software and tools to present data in a way that is easy for non-technical stakeholders to understand
- Communicating with stakeholders to gather requirements and provide recommendations
Required Skills:
- Strong analytical and problem-solving skills
- Experience with data visualization software and tools such as Tableau, D3.js, and R Shiny
- Strong communication and presentation skills
- Skills in Python and R, Tableau, and Power BI
Average Salary:
- Glassdoor shows that the average data visualization engineer salary in the US is $106,000.
Career Prospects:
- Strong, as data visualization becomes increasingly important for organizations to make sense of large sets of data.
Course to start with:
- We have a range of tracks and courses covering data visualization, including in technologies such as Python, Power BI, R, and Tableau.
Careers compared
In the table below, we've compiled the key information about each data analytics career to help you compare:
Career | Job Responsibilities | Required Skills | Average Salary (USD) | Career Prospects |
Data Scientist | Collecting, analyzing, and interpreting large sets of complex data; using statistical and machine learning techniques to identify patterns and trends. | Python, R, statistics, and machine learning. | $123,069 | Strong; 36% growth projected (2023–2033). |
Business Intelligence Analyst | Collecting and analyzing data; using data visualization software to present insights to stakeholders. | Power BI, SQL, Tableau, Excel. | $101,000 | Strong; 11% growth projected (2023–2033). |
Data Engineer | Designing and building infrastructure for data collection and storage; ensuring data is accurate and accessible. | Python, SQL, Java, Hadoop, Spark. | $127,315 | Strong; high demand for data engineers continues. |
Business Analyst | Identifying and analyzing business problems; developing solutions to improve processes. | SQL, Tableau, Excel, project management. | $84,778 | Strong; 25% growth projected by 2030. |
Marketing Analytics Manager | Collecting and analyzing marketing data; improving the effectiveness of campaigns. | Python, R, SQL, Excel, data visualization tools. | $112,000 | Moderate; 8% growth projected (2023–2033). |
Financial Analyst | Analyzing financial data to inform decisions; creating forecasts and identifying trends. | Python, R, SQL, Excel, financial modeling. | $79,000 | Moderate; 9% growth projected (2021–2031). |
Quantitative Analyst | Using mathematical and statistical techniques for modeling financial and economic systems. | Python, R, mathematical modeling, quantitative skills. | $147,000 | Strong; 8% growth projected (2022–2032). |
Risk Analyst | Identifying and assessing potential risks; developing risk management strategies. | Python, R, SQL, risk analysis and management. | $85,000 | Strong; risk management market growing significantly. |
Data Governance Analyst | Developing and implementing data governance policies; ensuring data accuracy and compliance. | Python, SQL, Java, data governance expertise. | $113,000 | Strong; increasing importance of data governance. |
Data Visualization Engineer | Designing and developing interactive data visualizations; creating dashboards and visual reports. | Python, R, Tableau, Power BI, D3.js. | $106,000 | Strong; rising demand for interactive data visualizations. |
Top Analytics Career Skills
You’ll notice that many of the careers in analytics we’ve listed here have similar foundational skills. As well as basic analytical, problem-solving, and communication skills, you’ll also need a range of technical skills. We’ve highlighted some of the key ones below.
Programming languages
Ultimately, to work in data analysis you’re going to need to be able to work with several different programming languages. Although you might be trying to decide between SQL vs Python, for example, you’ll eventually need to master both to work in analytics.
Similarly, languages such as R and Julia are also part of most analysts' wheelhouse. Your best bet is to determine what kind of analyst you want to be and start learning the most relevant language for you. You can read about the top programming languages for data scientists in a separate blog post. Here's a quick summary:
- Python: Known for its versatility and ease of use, Python is widely used in data analysis, data visualization, and machine learning.
- Start with our Data Scientist with Python track, which includes courses on pandas, NumPy, and scikit-learn, providing a comprehensive introduction to Python for data analytics.
- SQL: Structured Query Language is vital for extracting and managing data from relational databases, a core task for most analysts.
- Learn the essentials with the SQL Fundamentals course or explore advanced queries in the SQL for Business Analysts skill track.
- R: This language is particularly popular in academia and among statisticians for its statistical modeling and data visualization capabilities.
- Try our Data Scientist with R track to build a solid foundation in R programming.
- Julia: For high-performance analytics and numerical computation, Julia is gaining traction among data scientists.
- Start with the Julia Fundamentals skill track.
Data visualization
To present your work with impact, you’ll need to be fluent in data visualization. Again, languages such as Python and R are often the preferred choice among data scientists, but tools such as Tableau and Power BI are also worth learning, as they are often used in businesses.
- Python Visualization Tools: Libraries like matplotlib, seaborn, and Plotly make Python a go-to language for creating custom, detailed visualizations.
- The Introduction to Data Visualization with Python course is a great starting point.
- Tableau and Power BI: These are powerful business intelligence tools often used in enterprise settings for creating dashboards and reports.
- Start with our Introduction to Power BI or Data Visualization with Tableau courses to learn these tools step by step.
- R Visualization: With libraries like ggplot2 and Shiny, R offers exceptional tools for creating publication-quality visualizations.
- Try the Data Visualization in R track to get hands-on practice.
Mastering data visualization not only boosts your ability to present findings but also helps you explore data patterns and trends during analysis.
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Data wrangling and cleaning
Raw data is rarely ready for analysis. Data wrangling—the process of cleaning and preparing data—is a critical skill for analysts to ensure accurate and reliable insights.
- Techniques include handling missing values, merging datasets, and converting data types.
- Learn these methods in the Cleaning Data in Python or Cleaning Data in R courses.
Data wrangling is often the most time-intensive part of the analytics workflow, making proficiency here a significant productivity booster.
Statistical analysis and machine learning
Analytics professionals must understand the principles of statistics to draw accurate conclusions from data. Many advanced roles also require knowledge of machine learning to build predictive models.
- Statistics: Courses like Introduction to Statistics in Python can help you build foundational knowledge.
- Machine Learning: Gain expertise in model building, evaluation, and deployment with Machine Learning Fundamentals track or explore specific tools with the Machine Learning with scikit-learn course.
Understanding statistical analysis ensures that your insights are grounded in solid evidence, while machine learning prepares you for advanced predictive and prescriptive analytics.
Final Thoughts
Data analytics is an exciting industry with all kinds of career opportunities for those with the right skills and mindset. Whether you're interested in data science, business intelligence, or emerging roles like AI ethics or MLOps, the demand for analytics professionals shows no signs of slowing down. With competitive salaries, strong career prospects, and impactful work, a career in data analytics is both rewarding and future-proof.
By building foundational skills in programming, data visualization, and statistical analysis, and continuously upskilling, you can carve out a successful career in this exciting and dynamic field. Ready to take the next step? Get started with the Data Analyst in Python skill track!
A writer and content editor in the edtech space. Committed to exploring data trends and enthusiastic about learning data science.
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