This data analyst job description is free to copy, edit and post today. It names real tools (SQL, Excel, Tableau or Power BI, Python) and real deliverables, so candidates can quickly tell if they fit. Below it: the day-to-day work, U.S. pay data from O*NET, and the questions worth asking in round one.
Data analyst job description template
What a data analyst does
Most of a data analyst's week goes to three things: getting data out of systems, checking that it's right, and explaining what it means. A day might start with a sales leader asking why win rates dropped in the Northeast, move to a SQL query, then to fixing a dashboard that broke when a source table changed. The output is rarely the query itself. It's a chart, a short summary or a two-minute answer in a meeting.
Analysts sit between the business and the data team: managers ask the questions, data engineers build the pipelines, and data scientists, where they exist, handle forecasting and models. O*NET's task list for this work leans on reporting: maintaining dashboards, documenting report specs and pulling trend data to support recommendations.
The job changes with company size. At a startup, one analyst may own everything from setting up the BI tool to defining revenue, with more time spent on cleanup because the pipelines are young. At a large company, analysts usually specialize in one area, such as marketing or supply chain, inside an established warehouse, so the work shifts toward stakeholder management and precise metric definitions.
Skills and qualifications
A bachelor's degree is the norm. In O*NET's survey for Business Intelligence Analysts, 68% of respondents said a bachelor's degree is required for new hires, 23% said a master's, and 5% said an associate's degree. Fields vary (statistics, economics, business, computer science), and no certification is required, so a work sample often matters more than the major.
Salary
O*NET doesn't list "data analyst" as its own occupation. The closest match it uses is Business Intelligence Analysts (15-2051.01), and O*NET shows wage data for that title from the broader Data Scientists group (15-2051). Based on 2025 Bureau of Labor Statistics wage data, the U.S. median for that group is $120,230 a year ($57.80 an hour). The 10th percentile is $67,240 and the 90th percentile is $199,130.
Because that group includes data scientists, treat it as a broad benchmark, not a precise analyst figure, and check your state on O*NET's local wage page (California's median for the group is $141,590). Whatever you settle on, post a pay range in the listing. A growing number of states and cities require it, and candidates skip posts without one.
Interview questions for data analyst candidates
- "Revenue on the main dashboard dropped 15% overnight. What do you check first?" A strong answer checks the data before the business: was the refresh complete, did a filter or source table change, are there duplicates or missing days.
- "When can a join make your totals too high?" Good candidates explain fan-out: joining to a table with several rows per key repeats values, so sums inflate. They'd catch it by comparing row counts before and after.
- "Conversion went from 3.1% to 3.4% after a change. Is that real?" Listen for sample size, a confidence interval or significance test, seasonality, and whether anything else changed.
- "Tell me about an analysis that changed a decision." The best answers name the decision, the data, how they checked it and what happened, in a clear STAR structure.
- "Explain this chart to our VP of Sales in two minutes." Use a real chart. Strong analysts lead with the takeaway and say what it can't show.
For 20+ questions with model answers, red flags and a scoring rubric, see our full data analyst interview questions. To turn those into a consistent scoring sheet for every interviewer, use our interview scorecard template.
Writing tips for this role
- Name one BI tool, not five. A list of every BI product suggests you don't know your own stack. Name yours and add "or similar," since good analysts switch tools in weeks.
- Drop the specific-degree filter. Requiring a statistics or computer science degree cuts out economists, scientists and career changers who write excellent SQL.
- Use the title candidates search for. People search for "data analyst" far more than internal names like "insights associate." If your team says "BI analyst" or "reporting analyst," include both titles.
- Say whose questions they'll answer. "Owns paid acquisition reporting for marketing" attracts the right people. "Supports cross-functional stakeholders" attracts no one in particular.
- Don't hide a data engineering job inside an analyst post. If the person must build pipelines from scratch, say so plainly, or hire for that role separately. Compare with our data scientist and business analyst templates to check you've picked the right title.
Screening data analyst candidates with AI
Analyst resumes tend to list the same tools, which makes them hard to rank. NTRVSTA scores and ranks every resume against your job description, then an AI interviewer runs a live first round, as a video avatar or audio only, that candidates can take at any hour. The interview adapts to each answer, getting harder when a candidate is doing well and easier when they struggle. Python is in the exam library, and questions can be customized per role, so you can add your own SQL scenarios.
Your analytics lead gets a score breakdown (technical skills, English proficiency, communication), video snippets of key moments and a shareable report before the exercise round. Before trusting any automated score, read how AI interviews are scored.
Questions
What's the difference between a data analyst and a business intelligence analyst?
The titles overlap heavily, and many companies use them for the same job. When they differ, a BI analyst spends more time building and maintaining dashboards and reporting systems, while a data analyst spends more time answering specific business questions with ad hoc analysis. If yours does both, use both titles.
Should a data analyst job description require Python?
Only if the analyst will use it in the first six months. SQL and a BI tool cover most analyst work. If Python matters for a specific task, like automating a weekly pull or running a statistical test, list it as a must-have and name the task. Otherwise list it as nice to have so you don't lose strong SQL candidates.
How long should a data analyst take-home exercise be?
Keep it to about two hours, use a small realistic dataset, and say what you'll score in advance. Long unpaid projects drive away candidates who already have jobs. A live 45-minute exercise works just as well.
Curious how an adaptive AI first round would handle your analyst pipeline? Book a 20-minute demo, or take a sample interview yourself first. For more templates, browse all our job descriptions or read how to write a job description.
