Argentina 
Brazil 
Mexico 
Colombia 
Data analyst postings vary more than most tech roles: some teams want someone glued to SQL and dashboards day to day, others expect the analyst to lean into statistical modeling closer to data science. The postings that attract the right candidates settle that scope upfront, naming the business questions the analyst will own, the data stack and BI tool they will work in, and which teams depend on their numbers.
The template below covers exactly that scope, ready to copy and adjust. It’s followed by what US companies are actually asking for in 2026 data analyst postings, how the role splits from data science and data engineering, interview questions written for whoever is running the interview, and a scorecard for comparing candidates side by side.
Data Analyst Job Description Template
Replace the text in brackets and delete any line that is not true for your team. The strongest descriptions start with the business questions the analyst will answer, not with a list of tools.
About the Role
We are looking for a [Senior] Data Analyst to help [teams] make better decisions about [product, growth, operations, or finance]. You will own analysis and reporting for [area], working with data in [Snowflake, BigQuery, or PostgreSQL] and building dashboards in [Tableau, Power BI, or Looker]. You will report to [role] and work closely with [stakeholders].
Responsibilities
- Turn business questions from [teams] into clear analyses and recommendations.
- Write SQL to explore, clean, and combine data from [sources].
- Build and maintain dashboards and reports in [BI tool] that teams use every week.
- Define and document key metrics so every team uses the same numbers.
- Analyze experiments and changes, such as [A/B tests, pricing changes, or campaigns].
- Find and flag data quality issues, and work with data engineers to fix them at the source.
- Present findings to technical and non-technical audiences, including leadership.
Must-Have Qualifications
- [3 to 5]+ years of experience in data analysis or a closely related role.
- Strong SQL, including joins, window functions, and aggregations.
- Experience with a BI tool such as [Tableau, Power BI, or Looker].
- Working knowledge of Python for analysis.
- Solid understanding of statistics for business decisions, such as significance and sampling.
- Clear written and verbal communication in English, including presenting to executives.
Nice to Have
- Experience with dbt or a cloud data warehouse such as [Snowflake or BigQuery].
- Experience designing or analyzing A/B tests.
- Experience with data in [your industry].
- Daily use of AI tools for analysis, with a clear process for checking their output.
What We Offer
[Compensation range, if you can share it], [location or time zone requirements], [benefits], and [how the team works day to day].
What US Companies Ask for in Data Analysts in 2026
The data analyst roles US companies asked BEON.tech to fill between 2023 and 2026, most of them senior, follow the growing demand the U.S. Bureau of Labor Statistics tracks for data roles. These are the patterns that repeat:
- SQL is non-negotiable. About four in five roles ask for it.
- Python is common. More than half of the roles ask for Python.
- A BI tool is expected. About two in three recent roles name at least one. Tableau appears most often, followed by Power BI and Looker.
- The modern data stack shows up. Close to half mention dbt or a cloud warehouse such as Snowflake or BigQuery.
- Business partnership is part of the job. More than half mention stakeholders or business teams, and about four in ten mention dashboards.
- Excel is less central than older templates suggest. About one in five roles mention it.
- Statistics and experimentation matter for some roles. About three in ten mention statistics, experiments, or A/B tests.
- AI tools are starting to appear. About one in four roles mention AI.
- Five years is the usual bar. Five years is the most common requirement, with a range from three to seven.
The takeaway for your description: lead with SQL, your BI tool, and the business questions the role will own. List Excel only if the team truly works in spreadsheets.
Data Analyst vs. Data Scientist vs. Data Engineer
These roles are often confused, and choosing the wrong title attracts the wrong applicants.
| Role | Main focus | Choose it when |
|---|---|---|
| Data analyst | Answering business questions with SQL, dashboards, and clear explanations. | Teams need reliable reporting, metrics, and analysis to make decisions. |
| Analytics engineer | Modeling and testing data in the warehouse, often with dbt, so analysis is reliable. | Analysts spend too much time cleaning data and metrics disagree across teams. |
| Data scientist | Statistical modeling, experiments, and predictive models. | You need forecasting, advanced experimentation, or machine learning. |
| Data engineer | Building and running the pipelines that move data into the warehouse. | Data is missing, late, or unreliable at the source. |
If your data is not yet reliable, an analyst will spend most of their time fixing it instead of analyzing it, that usually means the team needs a data engineer, not another analyst. Data engineers and data scientists solve different problems, so it helps to confirm which one the team actually needs before opening the role.
How the Role Changes by Seniority
A data analyst’s day-to-day changes more by seniority than most roles: mid-level analysts answer the questions they’re handed, while senior and lead analysts start deciding which questions are worth asking.
| Level | Scope | What to look for in the interview |
|---|---|---|
| Mid-level (2 to 4 years) | Answers well-defined questions and maintains existing dashboards. | Clean SQL, accurate numbers, and clear explanations of results. |
| Senior (4 to 7 years) | Owns analysis for an area, defines metrics, and shapes decisions with stakeholders. | Metrics they defined, analyses that changed a decision, and how they handled messy data. |
| Lead (7+ years) | Sets analytics standards, metric definitions, and priorities across teams. | How they aligned teams on one version of the numbers and built self-serve reporting. |
Data Analyst Interview Questions for the Interviewer
These questions are written for the person running the interview. Each one lists what a strong answer covers and a warning sign. There are no model answers to memorize. The goal is to hear how the candidate turns questions into reliable answers.
SQL and Data
Write a query that shows monthly retention for users by the month they signed up.
Two dashboards show different revenue numbers. How do you find out why?
Business Thinking and Metrics
How would you define an active user for our product?
A manager asks you to explain why sign-ups dropped last week. What do you do first?
Statistics and Communication
An A/B test shows a 3% lift after four days. Would you recommend shipping the change?
Explain a complex analysis you did to someone with no technical background.
Working With AI Tools
How do you use AI tools in your analysis, and how do you check the results?
A Practical Exercise
Give candidates a small dataset and a real business question, such as why a metric changed. Ask for the SQL they used, one or two charts, and a short written recommendation of no more than one page.
Allow AI tools, then ask how they checked the numbers. Keep a take-home under three hours, or run a shorter version live in 60 to 90 minutes.
Data Analyst Interview Scorecard
Score each criterion from 1 (no evidence) to 4 (strong evidence). Agree on the weights before the first interview, not after.
| Criterion | What a 4 looks like | Suggested weight |
|---|---|---|
| SQL and data skills | Writes correct, efficient queries and catches data quality problems. | 25% |
| Business thinking and metrics | Frames questions around decisions and defines metrics carefully. | 20% |
| Visualization and storytelling | Builds clear dashboards and leads with the answer. | 20% |
| Statistics | Interprets tests and trends correctly and states uncertainty. | 10% |
| AI-assisted workflow | Uses AI tools to move faster and verifies every number. | 10% |
| Stakeholder communication | Works well with business teams and handles conflicting requests. | 15% |
Hiring a Data Analyst Through BEON.tech
If you would rather skip sourcing and screening, BEON.tech places senior data analysts from Latin America who work in US time zones. See how BEON.tech helps companies hire senior data analysts.
