BI Analyst

What does a BI Analyst do?

A BI Analyst turns raw data into answers that guide business decisions. You’ll translate questions from stakeholders into metrics, queries, and dashboards that are accurate, fast, and easy to understand.

• Clarify business questions and define clear metrics/KPIs
• Write SQL to pull, clean, and join data
• Model data into facts/dimensions that are easy to analyze
• Build dashboards and visualizations for recurring decisions
• Run ad-hoc analyses and present insights
• Maintain data quality checks and documentation

Typical deliverables:

• Executive KPI dashboards with weekly/monthly views
• Ad-hoc analysis write-ups answering a specific question
• Semantic layer definitions and metric catalogs
• Data quality alerts and validation reports
• Playbooks/documentation for recurring analyses

Peek at a day-in-the-life

• 09:00 — Sync with product/finance on KPI definitions
• 10:00 — Write SQL to build a new fact table
• 12:00 — Validate numbers vs source-of-truth
• 13:00 — Design dashboard layout and iterate on charts
• 15:00 — Share draft, collect feedback, refine metrics
• 16:30 — Add tests, document assumptions, schedule refresh

Where you can work

BI Analysts are needed anywhere data informs decisions:

• Industries: e-commerce, fintech, SaaS, healthcare, logistics, media, retail
• Teams: product analytics, marketing analytics, sales ops, finance FP&A, operations, executive analytics
• Company sizes: startups (broad scope), scale-ups (fast iteration), enterprises (deep specialization)

Hiring expectations by level

Junior BI Analyst

• SQL: can query one or two tables, join basics, aggregate reliably
• Visualization: uses standard charts, basic dashboard layout
• Modeling: understands facts vs dimensions; needs guidance
• Process: follows defined data quality checks and documentation templates
• Communication: asks clarifying questions; presents simple findings

Mid-level BI Analyst

• SQL: optimizes complex joins; uses window functions and CTEs
• Modeling: defines table grain; designs small star schemas
• Dashboards: builds multi-page KPI suites with drill-downs
• Quality: sets up freshness/uniqueness checks; triages issues
• Stakeholders: manages roadmap; negotiates scope and trade-offs

Senior BI Analyst

• Owns metric definitions and semantic layer governance
• Leads cross-functional data initiatives and mentoring
• Architects models for scale and performance
• Communicates at exec level; ties insights to business impact
• Instills rigorous testing, documentation, and version control culture

Salary ranges

• Junior: ~$50k–$80k
• Mid-level: ~$75k–$120k
• Senior: ~$110k–$160k+

Varies by country/company; treat as rough ranges.

Skill map

• SQL — Query, join, aggregate, window functions for analysis and data preparation.
• Semantic Layer Concepts — Shared metrics and business logic for consistent reporting.
• Dimensional Modeling Basics — Star schemas with clear grain, facts, and dimensions.
• Dashboard Design — Layout, hierarchy, and interactivity for decision-making.
• Data Visualization Principles — Choose the right chart; reduce clutter; emphasize signal.
• Performance Tuning Basics — Reduce scans, pre-aggregate, filter early, and index appropriately.
• Business Requirements Gathering — Turn vague asks into measurable KPIs and acceptance criteria.
• Data Quality Checks — Freshness, completeness, uniqueness, and referential integrity.
• Version Control Basics — Track changes, review, and roll back safely.
• Documentation — Metric definitions, lineage, and assumptions.

Mini tasks for each skill

• SQL: Write a query returning daily active users and 7-day moving average.
• Semantic Layer: Define a single source-of-truth metric for Conversion Rate with formula and filters.
• Dimensional Modeling: Sketch a star schema for Orders with Customer and Product dimensions.
• Dashboard Design: Create a 1-page executive KPI dashboard with red/amber/green statuses.
• Data Viz: Redesign a cluttered chart to emphasize the key comparison.
• Performance: Show how limiting columns and pre-aggregating reduces query time on a sample table.
• Requirements: Turn “improve retention” into a metric with a precise event and window.
• Data Quality: Add checks for row counts, null rates, and referential integrity on a key table.
• Version Control: Open a PR for a metric change; include test updates and a rollback plan.
• Documentation: Publish a KPI one-pager: definition, owner, data source, refresh, caveats.

Who this is for

• People who enjoy blending business questions with technical problem-solving
• Strong communicators who like building clear visuals and narratives
• Detail-oriented learners who value consistency and data quality

Prerequisites

• Comfort with basic math, ratios, and percentages
• Beginner spreadsheet skills (filters, pivots, simple formulas)
• Willingness to learn SQL and simple data modeling

No SQL yet?

Start with SELECT, WHERE, GROUP BY, and JOIN. Learn window functions later.

Learning path

1. Start with SQL — Pull and aggregate data reliably. Mini task: compute weekly active users.
2. Dimensional modeling — Define grain and model facts/dimensions. Mini task: orders star schema.
3. Data visualization principles — Chart selection and emphasis. Mini task: redesign a bar chart.
4. Dashboard design — Layout and interactivity. Mini task: build an executive KPI panel.
5. Data quality checks — Add freshness/uniqueness tests to key tables.
6. Semantic layer — Centralize metric definitions and access rules.
7. Version control + documentation — Pull requests, changelogs, KPI one-pagers.
8. Performance tuning basics — Filter early, select needed columns, pre-aggregate.

How to know you’re ready to move on

• You can write a query that others can read and validate
• Your dashboard answers one concrete decision quickly
• You can explain a metric’s definition and caveats in one minute

Practical projects for your portfolio

• E-commerce Performance Dashboard: Revenue, AOV, conversion, and cohort retention. Outcome: a single page exec view with drill-down.
• Marketing Funnel Analysis: From impressions to paid users. Outcome: clear bottleneck identification with recommended next tests.
• Churn Risk Report: Define churn, build features, and a simple risk segmentation. Outcome: playbook with actions by segment.
• Data Quality Monitor: Freshness and row-count alerts for core tables. Outcome: issue log and remediation workflow.
• Metric Catalog: Semantic definitions for 10 core KPIs. Outcome: published metric pages with owners and formulas.

Interview preparation checklist

• Explain a metric’s grain and why it matters
• Write a GROUP BY query and a window function on the spot
• Design a star schema for a simple domain
• Pick the right chart for a question and justify it
• Walk through data validation steps and common pitfalls
• Tell a story with a 3-slide insight summary
• Discuss trade-offs: accuracy vs freshness, depth vs speed

Mock interview mini task

Prompt: “Sign-ups are flat but revenue is up. What do you check first?” Outline 3 hypotheses, 3 queries, and 3 visuals you’d use to validate.

Common mistakes and how to avoid them

• Vague metrics — Always define event, window, filters, and grain before querying.
• Overcomplicated dashboards — Start simple; prioritize 3–5 KPIs that answer one decision.
• No data validation — Add sanity checks before sharing numbers.
• Poor performance — Limit columns/rows, pre-aggregate, and cache where possible.
• Weak communication — Summarize insights first; then evidence; then caveats.
• Missing documentation — Publish a one-pager for each KPI/dataset you ship.

Next steps

Pick a skill to start and complete one mini task this week. Then build your first dashboard and take the exam to check your readiness.