Data Analytics Jobs: Roles, Skills, Salary, and Demand

Data analytics jobs turn raw organizational data into decisions, using SQL, Python, statistics, and visualization tools to answer questions the business has already asked. A data analytics job sits one step below a data science job in modeling complexity and one step above a reporting job in analytical depth. The most common data analytics roles are business intelligence analyst, analytics engineer, operations analyst, marketing analyst, and data visualization specialist. A career in data analytics starts with a bachelor's degree, and careers in data analytics span healthcare, finance, manufacturing, mining, retail, and energy. A data analytics career rewards the people who can build the data pipeline as well as query it.

The Bureau of Labor Statistics has no occupation code for "data analyst." People doing data analytics work are counted under data scientists (SOC 15-2051), operations research analysts (15-2031), and market research analysts (13-1161), and those three occupations report median wages of $120,230, $88,940, and $78,760 respectively as of 2025. That $41,470 spread is why one site tells you data analysts make $78,000 and another says $120,000. Both are citing real BLS data about different occupation codes.

What Is a Data Analytics Job?

What is a data analytics job? A data analytics job is a role focused on interpreting existing data to answer business questions, distinct from data engineering (which builds the systems that move data) and data science (which builds predictive models). So what is data analytics in practice? A data analyst spends most of the working week on four things: pulling data with SQL, cleaning and joining it, analyzing it in Python or R or a spreadsheet, and presenting the result to someone who will act on it. Data analytics roles carry different titles across employers, and those data analytics roles are counted under three separate occupation codes.

The Bureau of Labor Statistics classifies this work under three separate occupation codes, which is worth knowing before you compare job postings:

BLS occupation	SOC code	Median wage (2025)	Jobs (2025)	Growth 2025-35
Data scientists	15-2051	$120,230	275,600	35%
Operations research analysts	15-2031	$88,940	113,100	12%
Market research analysts	13-1161	$78,760	952,700	7%

What Types of Data Analyst Jobs Are There?

The main types of data analyst jobs are business intelligence analyst, analytics engineer, operations analyst, marketing analyst, data visualization specialist, and product analyst. These six types of data analyst jobs account for most postings, and each type is defined by the part of the business the analyst owns, at every level of seniority. Each of these data analytics roles draws on the same core skill set.

Business intelligence analyst. Owns the dashboards and the recurring reporting layer. Works in Tableau, Power BI, or Looker on top of a warehouse. This is the most common entry point.

Analytics engineer. Owns the transformation layer between raw data and the dashboard. Writes SQL models, usually in dbt, and enforces data definitions so two dashboards showing revenue agree. This role pays closer to data engineering than to reporting.

Operations analyst. Owns process and throughput questions inside a physical or logistical system: plant scheduling, fleet routing, inventory levels. BLS counts this as operations research analyst, at an $88,940 median.

Marketing analyst. Owns attribution, campaign performance, and customer segmentation. BLS counts this as market research analyst, at a $78,760 median across 952,700 jobs, making it by far the largest of the three occupation codes.

Data visualization specialist. Owns how analytical results get communicated. Narrower than a BI analyst, and more common in large organizations that separate the analysis from the presentation.

Product analyst. Owns behavioral data from a software product: funnels, retention, feature adoption, experiment readouts. Concentrated in technology companies.

Are Data Analysts in Demand?

Are data analysts in demand? Yes, data analysts are in demand, and the demand for data analyst roles is growing faster than the demand for almost any other occupation the Bureau of Labor Statistics tracks. Employment of data scientists is projected to grow 35% between 2025 and 2035, against 3% for all occupations, adding 95,400 jobs and producing roughly 24,800 openings a year over the decade. Operations research analysts are projected to grow 12% and market research analysts 7%, both above the all-occupation average.

Put the three occupation codes together and the demand for data analysts covers roughly 1.34 million jobs in 2025, growing by about 175,000 by 2035, with something on the order of 114,000 openings a year once replacement hiring is counted.

The growth is not evenly spread. It concentrates where organizations already have instrumented systems producing data faster than people can read it: healthcare systems, financial services, logistics networks, utilities, and industrial operations running sensor telemetry.

What Is the Job Outlook for a Data Analyst?

The job outlook for data analysts through 2035 is much faster than average across all three occupations BLS uses to count the work. The strongest outlook belongs to data scientists at 35% growth, which is the code most closely matched to analysts who do modeling as well as reporting.

Two things temper the headline number. First, 24,800 annual data scientist openings is a modest absolute figure for an occupation this visible, so competition for entry-level roles is real. Second, the roles growing fastest are the ones that combine analysis with engineering, which is why analytics engineer postings have grown faster than pure reporting postings. Analysts who can build the data pipeline as well as query it are the ones who move from the $78,760 end of the range toward the $120,230 end.

How Much Do Data Analysts Make?

How much do data analysts make? Data analysts make between $78,760 and $120,230 at the median, depending on which of the three occupation codes the job actually falls under. Entry-level analytics roles typically start below those medians; BLS reports the lowest 10% of data scientists earning under $67,240 and the highest 10% above $199,130.

For a South Dakota Mines frame of reference, the 2024-2025 First Destination Report puts the average starting salary across all Mines graduates at $76,805, with 94% of graduates placed in employment or continuing education within six months.

What Are the Data Analytics Job Requirements?

Data analytics job requirements start with a bachelor's degree plus demonstrable SQL, the working floor for data analytics jobs. BLS lists a bachelor's degree as the typical entry-level education for all three occupation codes.

The technical requirements that appear in most postings:

Non-negotiable. It is the single most common requirement in analytics job postings and the most common technical screen.
Python or R. Python is more common outside academia and research. Pandas, NumPy, and a plotting library cover most day-to-day work.
Distributions, hypothesis testing, regression, and enough sampling theory to know when a result is noise.
A visualization tool. Tableau, Power BI, or Looker. Which one matters less than being able to build something a non-analyst can read.
Spreadsheet fluency. Still the interchange format for most of the business.
Version control. Git, once the work moves from ad hoc queries into maintained models.

Where Do Data Analysts Work?

Where do data analysts work? Data analysts work across essentially every industry that runs instrumented systems, which is now most of them. The industries where data analysts work in the greatest concentration are the industries generating the most data, at companies of every size.

Healthcare. Clinical outcomes, readmission risk, staffing and capacity planning, claims.

Financial services. Fraud detection scored in milliseconds, credit risk, portfolio and market analysis, regulatory reporting.

Manufacturing and mining. Predictive maintenance from equipment sensor telemetry, yield and throughput optimization, ore grade modeling, fleet utilization. This is where analytics work overlaps directly with engineering.

Retail and supply chain. Demand forecasting, inventory positioning, pricing, personalization.

Energy and utilities. Load forecasting, grid telemetry, outage prediction, renewable output modeling.

Government and defense. Logistics, program evaluation, resource allocation, and the operations research work the discipline originally came from.

What Can You Do With Data Analytics?

What can you do with data analytics? With data analytics you can answer questions an organization could otherwise only guess at, and the highest value applications share one trait: a decision gets made faster or better because of the analysis.

Concrete examples of what you can do with data analytics in production:

Predict equipment failure before it happens. Vibration, temperature, and pressure telemetry from industrial machinery feeds models that flag a bearing about to fail, converting unplanned downtime into scheduled maintenance.
Detect fraud in real time. Transaction-level scoring blocks a fraudulent card authorization inside the payment flow, while the transaction is still being processed.
Forecast demand. Retailers and distributors position inventory ahead of demand instead of reacting to stockouts.
Target clinical intervention. Health systems identify patients at elevated readmission risk and intervene before discharge.
Optimize routing and scheduling. Operations research techniques reduce miles driven, fuel burned, and idle equipment hours.
Measure what marketing actually did. Attribution modeling separates campaigns that produced revenue from campaigns that were merely present when revenue happened.

Data Analytics vs Data Science: What Is the Difference?

Data analytics vs data science comes down to one distinction: data analytics answers questions about what happened and why, while data science builds systems that predict what will happen next. That is the cleanest line between data analytics and data science, and it drives everything else about the two career paths.

In a data analytics job you query a warehouse, build a dashboard, and hand a business stakeholder a number they act on. In a data science job you train a model, evaluate it, and deploy it so it produces predictions continuously without you. Data analytics leans on SQL, descriptive statistics, and visualization. Data science leans on Python, machine learning, and the engineering needed to run a model in production. Data analytics work is counted by BLS under operations research analysts and market research analysts; data science work is counted under data scientists, at a $120,230 median against $78,760 to $88,940.

Data analytics vs data science, side by side

	Data analytics	Data science
Core question	What happened, and why?	What will happen, and what should we do?
Primary output	Dashboards, reports, analyses	Models deployed into products or operations
Typical tools	SQL, spreadsheets, Tableau or Power BI, Python	Python, machine learning frameworks, cloud training and serving infrastructure
Statistics depth	Descriptive and inferential	Predictive and machine learning
Engineering depth	Queries and transformations	Pipelines, model deployment, monitoring
BLS median (2025)	$78,760 to $88,940	$120,230

How Do You Start a Career in Data Analytics?

Start a career in data analytics by getting a bachelor's degree that teaches SQL, statistics, and programming together, then building two or three projects on real data that you can walk someone through. The degree gets the interview. The projects get the job. Most careers in data analytics begin in a business intelligence or operations analyst role, and a data analytics career compounds quickly once you can build the pipeline as well as query it.

Learn SQL first. It is the fastest path from zero to useful and it is what you will be tested on.
Add Python and statistics together. Learning them separately produces people who can run a test but not code it, or code it but not interpret it.
Work on data that is genuinely messy. Clean tutorial datasets teach syntax. Real data teaches judgment, and judgment is what interviews probe.
Do an internship or co-op. It is the single highest-signal line on an entry-level analytics resume.
Learn to build the pipeline. Analysts who can engineer the data flow move into analytics engineering, which pays closer to the $120,230 end of the range.

Studying Data Analytics at South Dakota Mines

South Dakota Mines teaches data analytics as engineering work through the Data Science & Engineering BS, a 120-credit degree in the Department of Electrical Engineering and Computer Science currently pursuing accreditation from the Engineering Accreditation Commission of ABET. The program combines computer science, statistics, and mathematics, and it puts data engineering in the required core curriculum.

That emphasis matters for the salary range above. Most analytics training stops at querying and visualizing. Mines students learn to build the systems that ingest, clean, store, and serve the data, which is the part of the job that moves an analyst toward the analytics engineering roles at the top of the pay band.

What the outcomes look like. Across all programs, the 2024-2025 First Destination Report puts SD Mines at 94% placement within six months of graduation, an average starting salary of $76,805, and 192 employers hiring from 36 states. Data science and engineering students have been hired by Google, Amazon Web Services, NASA, John Deere, and GE HealthCare.

Where analytics shows up beyond the data science degree. Mining engineering students at Mines work with the same predictive maintenance and fleet optimization problems described above, applied to equipment telemetry. Computer science students can take the Artificial Intelligence and Machine Learning specialization. The analytics skill set is not confined to one program.

Students work on hands-on projects and capstones from early in the degree, and build resumes through faculty-led research and internships. Classes are small enough that faculty know students by name, on a campus in Rapid City, South Dakota, in the Black Hills.

Explore the Data Science & Engineering BS program page

Frequently Asked Questions

Q: Is data analytics a good career in 2026?

Yes. Data analytics remains one of the fastest-growing occupation groups the Bureau of Labor Statistics tracks, with data scientist employment projected to grow 35% between 2025 and 2035 against 3% for all occupations. Entry-level competition is real, so the graduates who place fastest are the ones with internship experience and projects on messy real-world data.

Q: Do you need a master's degree to be a data analyst?

No. BLS lists a bachelor's degree as the typical entry-level education for data scientists, operations research analysts, and market research analysts. A master's helps for research-heavy roles and for moving into data science, but it is not the entry requirement.

Q: How long does it take to become a data analyst?

Four years through a bachelor's degree is the standard path, and it is the one that leads to the widest range of roles. Career changers with an existing quantitative background can move in faster, but entry-level postings overwhelmingly ask for a degree.

Q: Do data analysts need to know how to code?

Yes. SQL is required in nearly every analytics posting, and Python or R appears in most. You do not need software-engineering depth, but you need to be able to write, read, and debug your own analysis code.

Q: What is the difference between a data analyst and a data scientist?

A data analyst explains what happened using existing data. A data scientist builds models that predict what will happen and deploys them into products or operations. The pay difference tracks that gap: BLS puts data scientists at a $120,230 median against $78,760 to $88,940 for the occupation codes that capture most analyst work.

Q: Which industries hire the most data analysts?

Market research analysts are the largest of the three BLS occupation codes at 952,700 jobs in 2025, which puts marketing, retail, and consumer products at the top by volume. Healthcare, financial services, manufacturing, logistics, and energy follow, and those industries tend to pay above the market research median.

Q: Does South Dakota Mines offer a data analytics degree?

South Dakota Mines offers the Data Science & Engineering BS, a 120-credit degree through the Department of Electrical Engineering and Computer Science that covers the analytics skill set plus the data engineering work most analytics programs leave out. The program is pursuing ABET accreditation from the Engineering Accreditation Commission.