Data Analyst Job Description
A complete data analyst job description template, covering the SQL and dashboarding skills ATS systems actually screen for.
Data Analyst is the most reliably scoped title in the data family. Where "data scientist" and "analytics engineer" vary wildly by company, a data analyst posting almost always means the same core loop: pull data with SQL, shape it into a dashboard or report, and explain what it means to someone who does not want to read a query. The work sits on top of infrastructure someone else built — a data engineer's pipelines, an analytics engineer's transformed tables — rather than infrastructure the analyst builds themselves.
The confusion happens at the edges. A posting titled "Data Analyst" that asks for building predictive models or owning the transformation layer is really describing a data scientist or an analytics engineer, and companies write it this way more often than the clean version suggests — usually because one person is expected to cover work that would be three separate roles at a larger company. The reverse also happens: "Business Intelligence Analyst" postings frequently describe the same SQL-and-dashboard work as this one, just scoped to a single BI platform the company has standardized on. If the requirements list is mostly SQL, spreadsheets and a BI tool, you are looking at a genuine data analyst role regardless of what the title says.
This is usually the first data role on a resume, and it shows in the hiring bar: a portfolio project and a SQL screen carry more weight than a degree, and a surprising number of postings marked entry-level still list two or three years of experience as a requirement. That mismatch is common enough to expect, not a sign you misread the posting.
Bellamyre · Austin, TX
Full-time · Hybrid
$58,000 – $82,000
About the role
Bellamyre is hiring a Data Analyst to join the customer insights team, turning product and billing data into the reporting that sales, marketing and leadership use to make decisions. You'll work primarily in SQL against our warehouse, building and maintaining the dashboards that answer "how are we doing" before someone has to ask.
This is a hybrid role based in our Austin office, three days a week alongside the analysts and engineers who own the data you'll be querying — close enough to ask a question in person, remote enough that heads-down analysis days still work.
What you'll do
- Write and maintain SQL queries against the production data warehouse to answer recurring and one-off business questions.
- Build and maintain dashboards that stakeholders can read without needing you to interpret them live.
- Partner with sales, marketing and support leads to figure out what they're actually trying to decide before pulling any data.
- Clean and validate incoming data, flagging quality issues to the data engineering team rather than quietly working around them.
- Document what each dashboard measures and where the numbers come from, so it survives you being on vacation.
- Run basic statistical analysis — trends, cohorts, simple significance checks — without overstating what the data supports.
- Present findings in team meetings, including the uncomfortable ones where the data doesn't support what someone hoped.
- Maintain a backlog of ad hoc requests and set realistic expectations on turnaround.
What we're looking for
- One or more years of hands-on SQL experience — coursework and a portfolio project count if a job doesn't.
- Comfort with a BI tool such as Tableau, Power BI or Looker; we'll teach you ours if you know a different one well.
- Enough spreadsheet fluency to sanity-check a number before it goes into a dashboard.
- Clear written and verbal communication — you'll explain data to people who do not want a methodology section.
- Basic statistics: you can describe what a trend means without claiming more certainty than the sample supports.
- A track record of following a request through to a real answer, not just a first-pass query.
Nice to have
- Experience with Python or R for analysis beyond what a spreadsheet or SQL query alone can do.
- Exposure to a modern cloud data warehouse such as Snowflake, BigQuery or Redshift.
- A completed certificate or bootcamp in data analytics, if you didn't study it formally.
- Prior experience on a customer-facing or revenue-adjacent team, so you already know what sales and marketing tend to ask for.
- Familiarity with dbt or another transformation tool, even just reading models rather than writing them.
Benefits
- Medical, dental and vision coverage, with employee premiums covered in full.
- 401(k) with a 3% company match.
- Hybrid schedule: three days a week in the Austin office.
- $1,000 annual learning budget for courses, certificates or conferences.
- Eighteen days of paid time off plus company holidays.
Salary range
As posted for this sample role. Real pay varies by employer, location and experience.
$58,000–$82,000/ yr
ATS keywords for this role
The applicant tracking system (ATS) — the recruiting software a hiring team searches and filters applicants with — will screen for these. Weight shows how central each one is to this specific posting.
Required and central (3)
Important (7)
Mentioned in passing (8)
Related roles
Frequently asked questions
What's the difference between a data analyst and a data scientist?
Scope and tooling, mostly. A data analyst answers questions with SQL and a BI tool against data someone else prepared; a data scientist builds and validates models and is expected to code well enough to test them properly. In practice the line blurs at smaller companies, where "data scientist" sometimes just means a data analyst who also knows Python.
Do I need a degree in statistics or a related field?
Less often than the title suggests. Employers increasingly accept a certificate, a bootcamp, or self-taught SQL and BI skills backed by a portfolio project, especially at the entry level. A quantitative degree still helps at companies with formal analyst tracks, but it is rarely a hard requirement.
Why do so many "entry-level" data analyst postings ask for years of experience?
Usually because the requirements list was written aspirationally, or cloned from a mid-level posting without editing the experience line. If everything else in the posting reads entry-level — SQL fundamentals, dashboarding, no team-lead duties — it's normally worth applying anyway.
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