Data career paths: 2025 job guide
A list of seven major career paths you can pursue, including salary information, key skills, portfolio ideas, and career advancement options.
Nov 26, 2025 • 15 Minute Read
Looking to get into the field of data? There are many different career moves available, both advancing within your chosen domain or shifting to a new one. Here is a comprehensive list of the career paths available in 2025.
* Note: All average salary data is in USD and sourced from Indeed as of August 2025. Salary ranges will differ depending on your country and state.
Pluralsight’s 2025 career roadmap series
1. Data analytics
Data analysts (also known as business analysts or business intelligence analysts) are responsible for collecting, cleaning, organizing, and interpreting data to answer business questions and solve problems. They often work with databases and spreadsheets, prepare presentations and reports for colleagues, and create and maintain dashboards. They are able to see patterns where others see raw data.
Who would thrive
If you’re not only good at understanding patterns and numbers, but also explaining these to others, you’ll likely thrive in data analytics. This role is also great for people who collaborate well across teams and departments, and who get satisfaction from empowering others.
Typical salary range
The average salary range for a data analyst is between $51k to $146k per year.
Tools and technologies to know
Key programming languages: Having hands-on SQL experience is considered a baseline requirement for many jobs, followed by Python and R.
Popular libraries: Learn Pandas, Matplotlib, and plotly.
Dashboard software: You’re going to be building intuitive dashboards for yourself and others, so it helps to be familiar with Tableau, Power BI, and similar solutions.
Handling data: Learn about foundational concepts like data pipelines, data cleaning and preprocessing, and ETL. Also study up on data warehouse solutions like Snowflake, BigQuery, and Redshift.
AI/ML knowledge: Analyst roles are learning more into leveraging AI, so it can be helpful to know about these solutions. Some roles may want you to be familiar with ML model development, even though this has historically been more of a data scientist role.
Valued mindsets and soft skills
Pattern recognition and analysis: This is the bread and butter of your role.
Critical thinking: You need to question the data, your own assumptions, and that of others, otherwise your analysis may lead to uninformed or misinformed decisions.
Continuous learning: You’ll be constantly upskilling in SQL, Python, and R, as well as the dashboards and other solutions you’re using.
Data storytelling: I’ve written a whole tutorial on this one, and nowhere is this skill more important than data analytics. You need to be able to tell stories with and about data.
Collaboration: This field is inherently collaborative, because you’re getting requirements and empowering others with your solutions and insights.
Curiosity: The best insights start with the right questions.
Possible career pathways
There’s a considerable number of options, since data analytics is a common entry-level role to get your start in the field.
Within data analytics itself, you can specialize in a domain-specific area (e.g. marketing, product, health care, finance) and make a career out of this.
You can progress to a higher level role (e.g. senior data analyst, analytics manager, director of analytics).
From data analytics, you can move into pretty much any other specialization (e.g. data science, data engineering, MLOps, data architecture).
Outside of data analytics, you can also shift into software development or cloud computing roles, as there’s overlap with the tools and languages used.
Portfolio idea
Create a GitHub portfolio, then select a publicly available data set. Perform data cleaning and analysis on trends using SQL and Tableau, then upload this to your GitHub repository. Kaggle has data sets you can use.
NOTE: I would highly recommend checking out this list of example github portfolios for data professionals, particularly Katie Huang’s, as this is a superb demonstration of how to go about this.
2. Data science
Data scientists develop, implement and test new theories and processes to empower an organization. They often perform more complex analysis than a data analyst, running experiments and building predictive and prescriptive models.
Who would thrive
Anyone who enjoys intellectual challenges, coding, discovering things that nobody else knows, and constantly learning new things. If you love machine learning and algorithms, data science may also be a fit for you.
Typical salary range
The average salary range for a data scientist is between $80k to $272k per year.
Tools and technologies to know
Key programming languages: Having hands-on SQL experience is considered a baseline requirement for many jobs. Python, R, Scala, and Java are also popular.
Machine learning model learning methods: You should know the difference between all the different approaches, such as supervised and unsupervised.
Popular libraries: Learn Pandas, Matplotlib, NumPy, scikit-learn, plotly, and Pytorch/TensorFlow.
Deep data handling knowledge: You should be intimately familiar with ETL processes, data pipelines, data processing, and data analytics.
Predictive and prescriptive modelling: You’re going to be dealing with models. A lot.
Advanced statistics and algorithms: Math. Just math.
Valued mindsets and soft skills
Pattern recognition and analysis: Once again, if you’re in the field of data, this is an essential soft skill.
Creativity: You’re coming up with theories and solutions, so it helps to think outside the box.
Critical thinking: You need to question the data, your own assumptions, and that of others, otherwise your analysis may lead to uninformed or misinformed decisions.
Continuous learning: Any field in IT requires this, but when you’re involved with machine learning and programming, this is dialled up to eleven.
Data storytelling: There’s no point knowing a secret pattern if you can’t properly share it with others. You need to be able to tell stories with and about data.
Collaboration: You’ll be talking to stakeholders, finding their issue, communicating with others to help implement your models, and sharing results.
Curiosity: The best insights start with the right questions.
Possible career pathways
Data science is a deeply challenging and rewarding field. Because it requires a high degree of analytical, technical, and communication skills, these lend themselves well to stepping sideways into most data and IT careers. If you have a deep interest in markets, you can also shift into a role as a quantitative analyst.
Portfolio idea
Build a model to predict house prices or rental rates then upload it to GitHub. Use real datasets from Kaggle to get started.
3. Data engineering
Data engineers write the code to create, manage, and monitor data pipelines. They are responsible for designing and maintaining these systems, and ensuring data flow is optimized. Data engineers work closely with data scientists and analysts, who are traditionally their end users. They help transform data into valuable insights.
Who would thrive
If you’ve got strong analytical, problem solving, and communication skills—and obviously love data—then you’ll likely enjoy working in data engineering.
Typical salary range
The average salary range for a data engineer is between $83k to $176k per year.
Tools and technologies to know
Data fundamentals: You should know the types of data, and how data can be manipulated, analyzed, and stored. Know the difference between a data lake and a data swamp, or how to set up an ETL pipeline.
Programming and data query languages: Python, SQL, DAX, Java, and Spark are worth learning.
Relational database, cloud computing, and big data technologies: Learn about solutions like Hadoop, Kafka, Amazon Aurora, MongoDB, and Amazon RDS. Cloud will be a huge part of your role.
DevOps and source control tools: Familiarize yourself with Git, CI/CD, AWS DevOps, and Infrastructure as Code (IaC).
Data security, compliance, and privacy principles: There’s a good chance you’ll be dealing with sensitive data, and knowing the fundamentals will help you know how to identify and handle it.
Valued mindsets and soft skills
Analytical thinking: A given for any career involving data.
Communication and collaboration: You’ll be working closely with other data professionals, so this isn’t a lone-wolf career.
Problem solving: Inherently, data engineering is about solving problems and troubleshooting the solutions you implement.
Continuous learning: The tools and techniques available to you will be constantly shifting, especially when you’re working with cloud solutions.
Possible career pathways
While there’s no one path to data engineering, this is typically a career people select after working as a traditional software engineer first. Advancement may look like becoming a Lead Data Architect, VP of Data Engineering, or Chief Data Officer (CDO). Lateral progression might involve becoming a data scientist, lead software manager, or AI/ML developer.
Portfolio idea
Building an ETL pipeline with open data is typically a great portfolio project for beginners, such as extracting data from a CSV file, cleaning and transforming it using Python, and loading the transformed data into a cloud-based data warehouse.
4. AI/ML engineering
AI and machine learning engineers create and deploy smart systems that help software think, learn, and make decisions. The job covers a lot—some ML engineers take existing algorithms and make them run smoothly in live apps, while others (like AI researchers) experiment with brand-new ways of teaching computers.
Day-to-day, you'll team up with data scientists to figure out what kind of models you need, clean up messy data, train models with tools like TensorFlow or PyTorch, and then plug these smart features into real products or services.
Who would thrive
If you love math, data, and solving complex analytical problems, AI/ML will be exciting for you. People who come from backgrounds in computer science, math, statistics, or even physics usually do great because the role mixes coding and analytical thinking.
Typical salary range
The average salary range of an AI/ML engineer is between $84k to $201k per year.
Tools and technologies to know
Build foundational knowledge: Understand the basics of machine learning, such as supervised vs. unsupervised learning, and algorithms like linear regression, decision trees, and neural networks.
Learn ML frameworks: Get proficient with one or two libraries such as TensorFlow, PyTorch, or scikit-learn.
Data engineering skills: Surprisingly, a lot of ML work is data wrangling. Improving your Python skills (especially with pandas for data manipulation) and learning how to use databases or big data tools can set you apart.
Math and theory: Depending on how deep you want to go, brushing up on linear algebra and calculus is useful.
Valued mindsets and soft skills
Continuous learning: AI moves faster than most fields, and there are always new models and solutions to learn about.
Problem solving: A love of solving tricky problems will serve you well in AI/ML.
Analytical thinking: Being able to break a whole problem down into smaller parts and take an in-depth look is a valuable skill.
Possible career pathways
There are a lot of career opportunities within this field, such as:
Computer vision engineer
NLP engineer
AI research scientist
Robotics engineer
AI ethics consultant
AI product manager
AI architect/strategist
In terms of lateral moves, AI/ML professionals can easily sidestep into careers in data science, mathematics, developer relations, or mainstream development.
Portfolio idea
Try projects like creating a simple image classifier, a sentiment analysis tool, or a movie recommendation system. Use real datasets from Kaggle to get started.
5. Quantitative finance analysis
Quantitative analysts (or "quants") use statistical models and advanced mathematics to understand, forecast, and optimize financial outcomes. They’re often tasked with pricing complex derivatives, managing risk, and building algorithmic trading strategies. While traditionally based in finance and insurance, quants are increasingly found in hedge funds, asset management firms, fintech, and crypto. In short: a domain-specific data scientist, heavy on math.
Who would thrive
If you have a strong background in mathematics, statistics, or physics—and you’re excited by the idea of applying these in financial markets—you’ll likely thrive as a quant. This role suits detail-oriented problem solvers who enjoy high-stakes environments and are comfortable working with ambiguity, models, and large datasets.
Typical salary range
The average salary range for a quantitative finance analyst is between $78k to $249k per year.
Tools and technologies to know
Programming languages: Python and SQL are used heavily, with R for quantitative research. Models are also implemented in C++ to minimize latency. However, you’re going to find people using Julia, Java, and whatever the situation requires.
Knowledge of market fundamentals and operations: This deep domain experience is one of the things that separate you from data scientists.
Statistical modeling and numerical analysis: Self explanatory. You’ll want to know regression analysis, time series modeling, stochastic calculus, Monte Carlo simulations, and other numerical methods used in the field.
Quant libraries and packages: There’s a great public list of Quant libraries here.
Financial modeling tools: Look into tools like Quantrix, Oracle BI, QuantLib (Open source), and others.
Data handling: Understanding of financial market data structures and data feeds.
Valued mindsets and soft skills
Analytical thinking: You’re going to be staring at a whole bunch of financial data, so precision, accuracy, and mathematical thinking will be key.
Critical thinking: True of all data professionals, but especially when dealing with a field where decimal-point differences can cost considerable money.
Continuous learning: The world of finance moves not monthly, but daily and hourly. The tools and techniques you’re using will always be shifting as well. It’s not boring, but it’s certainly not for everyone.
Adaptability and resilience: Things move fast, stakes can be high, and you’re under a lot of pressure. Being able to pivot and roll with things is a valuable trait.
Communication and collaboration: Being able to explain things to stakeholders and traders will be important.
Possible career pathways
Within quantitative analysis, typical roles include becoming a statistical analyst, model validation analyst, risk management officer, or front office quantitative analyst. With experience, you can move into more senior or specialized roles such as quantitative portfolio manager, head of quant research, or chief risk officer. Lateral moves include moving into data science, AI/ML engineering, academia, or consulting.
Portfolio idea
Using ORE, implement a deep learning pricer for select options, using a closed formula for risk. Demonstrate how you’d integrate a deep learning library, how you’d train it, and serialize/deserialize the state.
6. Data architecture
As the name suggests, data architects are responsible for designing and building a company’s data infrastructure. They map out a company’s infrastructure needs, architecting databases and data warehouses, building data pipelines, and designing robust and scalable solutions. While a data engineer is responsible for hands-on building and maintenance, architects are more high level and strategic. Put plainly, an architect designs the blueprints, and an engineer builds the house.
Who would thrive
Anyone with existing experience in other data-related disciplines who has leadership skills and can take a step back to see the larger picture.
Typical salary range
The average salary range for a data architect is between $92k to $242k per year.
Tools and technologies to know
Programming and data query languages: Python and SQL are practically a given, with other languages a plus.
Deep data knowledge: You should have significant knowledge of database systems, data modelling, data governance, data security, and compliance. Nothing is off limits.
Cloud computing: A lot of your role is going to require knowing about cloud services, since this will impact how you think about building data architecture. Pick your platform such as AWS, Azure, or GCP, and dive into this area.
Valued mindsets and soft skills
Systems thinking: Data architecture can quickly get complex, so being able to figure out how different systems should work together is a core skill.
Problem solving: There will be many ways to solve a problem, but not all solutions will be equal. Great architects can pick and apply the best one.
Leadership, communication, and collaboration: You’ll be leading people towards your vision of the ideal data architecture while listening to the needs of the business.
Holistic thinking: Great architects think of what the final result should be and the wider workings and demands of the business.
Critical thinking: Questioning your current or proposed solutions, or those of others, is vital to picking the best path forward.
Continuous learning: You need to be the one who’s up to date with the services and best practices currently available to build the ideal solution for your business, so there’s never a shortage of things to keep up to date with.
Possible career pathways
Data architecture can have many senior roles such as the principal or lead architect, or chief data officer. It’s also quite easy to step from data architecture to systems and cloud architecture roles.
Portfolio idea
As an architect, write up your problem, solution, and architecture diagram in a GitHub readme file, then include all the components for the architecture you propose. This includes any Terraform or CloudFormation scripts.
7. Data governance
Data governance specialists are responsible for making sure business data maintains confidentiality, integrity, and availability (CIA). They create, deliver, and maintain policies around data governance, typically working alongside a company’s cybersecurity and legal departments.
As stewards of data, they ensure a static approach is taken to data quantity and security, rather than tactical cleaning efforts. Depending on the organization’s size, they may work in a dedicated data governance team.
Who would thrive
People who are compliance-driven, love driving efficiency and best practices, and work well with others.
Typical salary range
The average salary range for a data governance specialist is between $67k to $216k per year.
Tools and technologies to know
Deep understanding of data privacy regulations: This will vary depending on your country and industry, but can include things like the GDPR, CCPA, UK Data Protection Act, EU Data Governance Act, SOX, NIS, EU Digital Markets Act, GLBA, and HIPAA.
Data governance policies and procedures: You should understand best practice around data quality, categorization, documentation, compliance, and privacy.
Data quality and automation tools: It helps to be familiar with solutions like Informatica DQ, Collibra, Alation, Secoda, and Talented.
Security knowledge: There’s a lot of crossover between data governance and cybersecurity governance, risk, and compliance (GRC). It can be helpful to be familiar with a wide range of security concepts.
SQL and data profiling knowledge: Not always on the job description, but these can help you get and thrive in data governance roles.
Valued mindsets and soft skills
Stakeholder engagement and relationship building: A huge part of your role will involve getting buy-in from stakeholders to ensure they properly adhere to policy and best practices. Engaging with and building up allies is a vital skill.
Communication and collaboration: Being able to effectively communicate will be key to success.
Continuous learning: You are going to need to be keeping up with a wide range of regulatory changes and informing others, so love of learning is a must.
Problem solving: You’ll be putting out fires and solving pain points to keep things moving.
Empathy: Asking people to adopt secure best practices often requires an empathic touch, as this often means more work for the people involved.
Critical thinking and “The Security Mindset”: You’re looking at existing processes and thinking about how it could be done better. Additionally, having the ability to see how data could be intentionally targeted by bad actors (The Security Mindset) is valuable.
Creativity and strategic thinking: Governance specialists know how to reduce risk through great design and simplicity, not just applying controls.
Possible career pathways
Data governance can be a great pathway to working as a data analyst or building up your skills to work as a data engineer. Since data governance has a lot of crossover with cybersecurity, it can be an excellent foot in the door to start in that space, particularly as a GRC specialist. Consultant roles are a popular career path for senior governance specialists.
Portfolio idea
Follow the same steps in this article for a data analytics portfolio, but focus on demonstrating how you adhered to data governance principles. For example, in the GitHub readme section:
Clearly describe your data sources and methods
Highlight the data security and privacy practices you used
Explain how you complied with data ethics and values
Show how you ensured data quality and accuracies
Showcase the learnings and feedback
Future considerations: 2026 roles that are on the rise
AI Evaluations Analyst: LLM evaluations, red-teaming, rubric design, human-in-the-loop QA.
Data Quality Engineer: Tests, anomaly detection, expectations, SLAs.
DataOps Engineer: Release/orchestration, runbooks, incident management for data.
Feature Engineer: Feature stores, online/offline parity, real-time features.
Geospatial Data Scientist/Engineer: Location, mobility, logistics.
Streaming/Real-Time Analytics Engineer: Kafka/Flink, sub-second metrics.
Revenue/Marketing Science Analyst: MMM, MTA, incrementality.
How these data roles can overlap
With most professions, your role responsibilities scale depending on the size of the company, and data is no exception. In a smaller company, data practitioners may act as analysts who also handle ETL and other tasks. Meanwhile, in a larger company, there’s often more room for niche, siloed data roles.
Long story short? Don’t be surprised if jobs at non-enterprise companies involve you wearing more than one of the hats listed above. Depending on your career goals this can be a good thing, because it gives you more opportunities to develop a wider range of skills, and by extension, increase your career opportunities.
Conclusion
Hopefully this guide has acted as a valuable dataset for you to make your next career decision! There are many different fields in the data domain other than just being a data analyst. Make sure to pick the one that fits you best—while some fields pay better than others by default, success can be found in any of the specializations listed above.
No matter what path you choose for your data career, or even if you choose something entirely different, I wish you the best of luck!
Getting your data career started
If there’s one common thread that all data careers share, it’s a need for continuous learning to both get started and stay ahead. One way to get started is to take one of Pluralsight’s beginner, intermediate, and expert data courses. Since you can sign up for a 10-day free trial with no commitments, it’s a great way to take some professionally authored courses with a set course structure. Why not check them out?
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