Business Intelligence Analyst Job Description
A business intelligence analyst job description template: the ATS keywords recruiters screen for, a realistic salary range, and what the title covers.
Business Intelligence Analyst is one of the most tool-fragmented titles in the data field. What the job actually looks like week to week is set almost entirely by which BI stack a company already runs — Tableau, Power BI, Looker, or something built in-house — and postings rarely lead with that. Two "BI Analyst" roles at similar companies can require near-opposite toolsets, which is why the requirements section matters more here than the title does.
The other title this one blurs into is Data Analyst. At most companies the two are used interchangeably, and where a distinction is drawn, it's usually that a BI Analyst owns recurring, productionized dashboards and metric definitions, while a Data Analyst does more one-off investigative work. That line moves company to company, and mostly disappears at smaller ones, where one person often does both plus a share of what an Analytics Engineer would own elsewhere.
The skill that separates a mid-level BI Analyst from an entry-level one is rarely a harder tool — it's judgment about definitions. Knowing that "active user" needs one definition for the board deck and a stricter one for the product team, and being the person who notices two dashboards disagree before an executive does, is most of what the seniority step actually buys a company.
Bellamyre · Remote (US)
Full-time · Remote
$80,000 – $110,000
About the role
Bellamyre runs on its own numbers as much as it sells access to other companies' numbers. We're hiring a Business Intelligence Analyst to own the dashboards and reporting that the go-to-market and product teams check every week — pipeline, retention, product usage, the metrics people make real decisions from.
This is a fully remote role reporting into the Analytics team. You'll spend more time than you might expect on definitions — making sure "active account" means the same thing in three different dashboards — and less time than you might expect on one-off analysis. Most of the job is building things that stay right after you've moved on to the next request.
What you'll do
- Build and maintain the recurring dashboards and reports that go-to-market and product teams check every week.
- Translate an ambiguous question — "why did signups dip in March" — into a scoped SQL query and a clear, defensible answer.
- Own definitional consistency for core business metrics, so "active account" means the same thing in every dashboard that uses it.
- Partner with analytics engineering on the modeled tables dashboards are built from, and flag where a model doesn't match what the business actually means.
- Support monthly and quarterly business reviews with accurate, on-time reporting.
- Investigate anomalies in key metrics and distinguish a real trend from a tracking bug or a one-time event.
- Document dashboards and metric definitions clearly enough that a report still makes sense to someone who didn't build it.
- Field ad hoc analysis requests from sales, marketing and finance, and point recurring ones toward a self-serve dashboard instead.
What we're looking for
- Two to four years in a BI, data analyst, or reporting-focused analytics role.
- Strong SQL — joins, window functions, and the ability to read a slow query and know why it's slow.
- Hands-on experience with at least one BI or visualization tool, such as Tableau, Power BI or Looker.
- Experience turning a business question into a well-scoped analysis and explaining the answer to a non-technical audience.
- Comfort owning metric definitions and data quality, not only building charts against whatever table already exists.
- Spreadsheet fluency, Excel or Google Sheets, for the analysis a BI tool is the wrong size for.
Nice to have
- Experience with dbt or another transformation tool, even at a "wrote a few models" level.
- Familiarity with a modern cloud data warehouse — Snowflake, BigQuery or Redshift.
- Basic Python or R for analysis that has outgrown a spreadsheet.
- Experience in a subscription or SaaS business, where metrics like churn and retention have specific, non-obvious definitions.
- Enough statistical literacy to know when a small sample doesn't support the confident claim someone wants to draw from it.
Benefits
- Medical, dental and vision coverage, with employee premiums covered in full.
- 401(k) with a company match, vested immediately.
- Fully remote within the US, with a one-time home-office equipment budget.
- $1,500 annual learning budget for courses, certifications or conferences.
- Flexible PTO, with a two-week minimum the team actually enforces.
Salary range
As posted for this sample role. Real pay varies by employer, location and experience.
$80,000–$110,000/ 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 (8)
Mentioned in passing (8)
Frequently asked questions
What's the difference between a Business Intelligence Analyst and a Data Analyst?
At most companies, not much — the titles are used interchangeably. Where a real distinction is drawn, a BI Analyst tends to own recurring, productionized dashboards and metric definitions, while a Data Analyst does more one-off investigative work. That line moves by company, and mostly disappears at smaller ones.
Do I need to know Python for a Business Intelligence Analyst role?
Usually not as a requirement. SQL plus a BI tool covers most of the day-to-day work. Python or R shows up as "preferred" for analysis that a dashboard or spreadsheet can't handle, and becomes more central if the role blends toward data science or analytics engineering.
Which BI tool should I learn if I'm trying to break into this field?
There's no single standard — Tableau and Power BI are the most commonly listed, and Looker turns up often at companies already built on Google or GCP infrastructure. Pick one BI tool and get comfortable with SQL against a real warehouse; the concepts transfer between tools faster than job postings make it seem.
Is Business Intelligence Analyst a good path toward Data Science?
Sometimes, but it isn't automatic. Day-to-day BI work touches statistics and modeling lightly at most, so someone aiming for Data Science usually has to build that layer deliberately, on their own time or through a lateral move. What BI does reliably build is SQL fluency and business context, which plenty of data scientists actually lack.
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