Data Analyst Job Description (+ 2025 TEMPLATE)

 

The right analyst can change everything.

Not just your dashboards. Your decisions. Your speed. Your team’s confidence.

You don’t need more reports. You need better ones, faster. Ones that people actually use. And that starts before hiring begins. It starts with clarity. Clear role. Clear goals. Clear job description.

This guide helps you write the kind of data analyst job description that doesn’t just attract candidates—but filters the right ones in.

If you’re hiring a data analyst, you’re not looking for someone who just knows SQL. You’re looking for someone who knows what matters. This post shows you how to write that into the job from day one.

 

What Does a Data Analyst Do?

A data analyst doesn’t just “crunch numbers.” They translate raw data into actions, pulling insights out of chaos, and making them readable for real-world decisions.

At their core, data analysts gather, clean, analyze, visualize, and report. That’s the full loop. Whether it’s fixing messy spreadsheets or building interactive dashboards, the job is about helping people act faster and smarter.

Typical outputs include:

• KPI dashboards (weekly, monthly, executive-ready)
• Ad-hoc analysis for one-off questions
• A/B test or experiment results
• Forecasts, trend analysis, and business case support

These outputs might look like charts or tables. But the real work is what’s underneath—asking the right questions, testing assumptions, and telling a story with the numbers.

Here’s a complete guide of all you need to know about hiring a data analyst.

 

Where do analysts sit?

It depends on your team. Analysts often support:

• Product teams (feature usage, experiments, retention)
• Marketing (channel attribution, campaign ROI)
• Operations (SLAs, bottlenecks, resource planning)
• Finance (revenue trends, cost drivers, forecasts)

They’re cross-functional by nature. A good analyst knows how to speak in numbers and in business.

Want to understand exactly what analysts earn across roles? Check out our Data Analyst Salary Guide.

Need help figuring out which questions to ask in interviews? Here’s our Technical Questions for Data Analysts.

(Role definition based on O*NET OnLine – BI Analyst profile, the U.S. government’s database of occupational information.)

 

What Are the Responsibilities/Duties of a Data Analyst?

A strong data analyst job description is built around one simple truth: you’re not hiring someone to fill a seat, you’re hiring someone to answer questions. 

Fast, accurately, and in a way others can act on.

That’s why the best data analysts don’t just write SQL. They solve problems.

Here’s a checklist of key data analytics job responsibilities you can plug directly into your JD, tested across dozens of real-world teams, and aligned with the U.S. Department of Labor’s guidance from the Bureau of Labor Statistics.

Core responsibilities:

• Ingest and clean data from internal databases, APIs, spreadsheets, and third-party tools.
• Write queries and build data models using SQL (and often Python for deeper analysis).
• Explore data to identify trends, test hypotheses, and size opportunities or issues.
• Build dashboards and reports that deliver clarity, not clutter.
• Present insights and recommendations to cross-functional stakeholders.
• Maintain data quality through documentation, version control, and basic governance.

Every bullet above should align to a business outcome. If your analyst spends time cleaning data, it’s so your dashboards don’t break. If they’re analyzing experiments, it’s to guide product decisions. This isn’t just about skills, it’s about translating those skills into real business value.

 

What Skills Does a Data Analyst Need?

Writing a clear data analyst job description means knowing which skills are essential and which are just nice to have. 

Too many JDs try to list everything under the sun. The result? The right people scroll past, and the wrong ones apply.

Let’s keep it clean.

Core skills (non-negotiable):

These are the foundations of every solid analyst, whether they’re supporting a startup or scaling insights at an enterprise level:

• SQL – the language of data querying. They should write clean, efficient queries across large datasets. (See PostgreSQL documentation for a reference standard.)
• Data wrangling – cleaning, transforming, and shaping raw data into something usable.
• Descriptive statistics – mean, median, percent change, trends. This is the layer before the visuals.
• Data visualization – using tools like Power BI or Tableau to turn insights into action.
• Stakeholder communication – explaining what the numbers mean to people who don’t live in spreadsheets.

These core skills belong in every sample data analyst job description, whether junior or senior. They drive the value behind every insight your analyst delivers.

Nice-to-have (but worth screening for):

If your role supports product, marketing, or finance, these extras may make all the difference:

• Python or R – for advanced analysis, automation, or notebooks.
• A/B testing – setting up experiments, analyzing results, and guiding product direction.
• Forecasting – predicting outcomes using historical data and trends.
• Domain knowledge – familiarity with your industry helps turn insights into action faster.

 

Data Analyst Job Description Templates (Copy & Paste)

Below are three complete data analyst job description templates—for junior, mid-level, and senior roles. Use them as-is, or tweak them to match your industry and tools.

Each template includes a summary, key responsibilities, required skills, KPIs, and a sample screening task to help you hire smarter from day one.

 

 

Sample Screening Task

Provide a messy CSV, ask them to clean it, write three basic SQL queries, and summarize the findings in five slides.

 

 

 

 

✅ Copy a Template → Talk to Us About Matching a Candidate

We’ve already sourced, tested, and vetted analysts across Nigeria, LATAM, and more, so if you’re ready to hire, we’ll get you there faster.

 

Frequently Asked Questions

• What does a data analyst do day-to-day?

They gather and clean data, run analysis, build dashboards, and explain what the numbers mean to teams. Their job is to turn raw data into useful decisions.

• Which skills are non-negotiable?

Strong SQL, comfort working with messy data, basic stats, and clear communication. These are must-haves in every data analyst job description.

• Which tools should I list?

Start with SQL (PostgreSQL or MySQL), then add tools like Power BI, Tableau, Excel, and optionally Python. Focus on tools your team actually uses.

• How do I assess real-world skills fast?

Give a short task with real data. Ask them to clean it, write a few queries, and present findings. You can use this data analyst test as a template.

• What’s the difference between a data analyst and a data scientist?

Analysts focus on understanding what happened and why. Data scientists work on prediction, automation, or machine learning models. Think: analysts explain, scientists predict.