This free Data Analyst job description template is ready to use — copy it, replace the {{placeholders}}, and post your role in minutes. It includes a company intro, a role summary, responsibilities, requirements, nice-to-haves, and compensation, with writing tips and FAQs below to help you tailor it to your team.
When to use this template
Use this when you're hiring someone to answer business questions with data — building dashboards, running analyses, and helping teams make decisions. It assumes a generalist analyst rather than a specialized data scientist or data engineer.
The most important thing to get right is the balance between technical skills (SQL, a BI tool) and business partnership. The best analysts are translators, so the JD should reflect that they'll work closely with non-technical stakeholders.
If the role is really about building data pipelines or ML models, use an engineering-leaning template and adjust the requirements — analyst candidates and data engineers rarely overlap.
Writing tips
- Be clear about the tools: SQL plus your BI stack (Looker, Tableau, Metabase, etc.).
- Emphasize stakeholder communication — analysts succeed by influencing decisions, not just querying data.
- Distinguish this from data engineering and data science roles to avoid mismatched applicants.
- Name the kinds of questions they'll answer; concrete examples attract the right candidates.
- Include the salary range and clarify the seniority level.
The job description
Copy the template below and replace the {{placeholders}} and [bracketed notes] with your specifics.
About {{company}}
{{company}} is [what you do]. As we grow, we're investing in our ability to make decisions with data — and we're hiring a Data Analyst to lead that work.
The role
As a Data Analyst, you'll turn raw data into clear answers that teams across {{company}} can act on. You'll build dashboards, run analyses, and partner closely with product, growth, and leadership to inform decisions. This role reports to {{hiring_manager}} and is based {{work_type}} in {{location}}.
What you'll do
- Answer business questions with rigorous, well-communicated analysis.
- Build and maintain dashboards and reports that teams actually use.
- Partner with stakeholders to define metrics and turn vague questions into clear ones.
- Identify trends, opportunities, and risks, and proactively flag them.
- Help improve our data quality, definitions, and self-serve reporting.
What we're looking for
- 2+ years in an analytics or data role.
- Strong SQL and experience with a BI tool ([Looker, Tableau, Metabase, Mode]).
- The ability to translate a business question into the right analysis.
- Excellent communication — you can present findings clearly to non-technical teams.
- A healthy skepticism about your own numbers and a habit of sanity-checking.
Nice to have
- Experience with [Python or R] for deeper analysis.
- Familiarity with experimentation and A/B testing.
- Background in a SaaS or product-led business.
What we offer
- Salary range: {{salary_range}}, plus equity.
- [Comprehensive benefits].
- Flexible {{work_type}} working and [PTO policy].
- A seat at the table where decisions get made — your analysis will shape what we do next.
How to personalize
Replace these placeholders before posting:
- {{company}}
- {{location}}
- {{work_type}}
- {{salary_range}}
- {{hiring_manager}}
The bracketed notes — like [your benefits] or [your primary language(s)] — are prompts to swap in your own details. The more specific you are about the actual work and stack, the stronger your applicant pool will be.
Frequently asked questions
- What does a Data Analyst do?
- A Data Analyst turns raw data into answers that teams can act on. They write SQL, build dashboards and reports, run analyses to answer business questions, and partner with stakeholders to define metrics and surface trends, opportunities, and risks.
- What's the difference between a Data Analyst and a Data Scientist?
- A Data Analyst focuses on describing what's happening and why — reporting, dashboards, and analysis that inform decisions. A Data Scientist leans more toward predictive modeling, statistics, and machine learning. The roles overlap, but analysts are generally closer to the business and scientists closer to algorithms.
- What skills should a Data Analyst have?
- Strong SQL is the foundation, paired with a BI tool such as Looker, Tableau, Metabase, or Mode. Just as important is the ability to translate a vague business question into the right analysis and to communicate findings clearly to non-technical stakeholders. Python or R is a common bonus.