What a BI Developer does
A BI Developer turns raw data into trustworthy reports, dashboards, and self-serve data models that help people make decisions. You’ll design data models, write SQL, define metrics, build visualizations, and keep everything fast, secure, and well-documented.
Day-to-day responsibilities
- Translate business questions into data requirements and clear metrics definitions.
- Design star/snowflake schemas for analytics and write SQL to prepare tables/views.
- Build dashboards and reports with filters, drilldowns, and accessible visuals.
- Optimize queries, models, and visuals for performance and reliability.
- Implement row/column-level security and handle access requests safely.
- Version control your work, review changes, and document logic and data lineage.
- Monitor data refreshes and fix issues before users notice.
Typical deliverables
- Well-modeled datasets (fact/dimension tables) ready for BI tools.
- Business-critical dashboards (e.g., Revenue, Funnel, Inventory, SLA).
- Metric definitions and a data dictionary people can understand.
- Performance tuning plans and incident post-mortems.
- Security rules (RLS/CLS) with tests and documentation.
Mini task: Define a KPI properly
Pick one KPI (e.g., Monthly Active Users). Write:
- Name and owner
- Business purpose
- Precise SQL/DAX definition (filters, time window)
- Source tables and refresh schedule
- Known caveats
Where you can work
BI Developers are needed everywhere data-driven decisions matter.
- Industries: SaaS, e-commerce, fintech, health, gaming, telecom, logistics, manufacturing, public sector.
- Teams: Data/Analytics, Finance, Product, Operations, Marketing, Sales, Customer Support, Risk.
- Company sizes: Startups (wear many hats), scale-ups (fast growth), enterprises (governance-heavy), consultancies (varied projects).
Hiring expectations by level
Junior
- Comfortable with SQL joins, aggregations, and basic data modeling.
- Can build clear dashboards from defined requirements.
- Understands basic performance and simple RLS patterns.
- Needs guidance for scoping and stakeholder communication.
Mid-level
- Designs robust star schemas; implements incremental refresh patterns.
- Leads dashboard/semantic model design end-to-end.
- Owns performance tuning and monitors data quality.
- Translates ambiguous asks into measurable metrics and roadmaps.
Senior
- Sets BI standards for modeling, metrics, governance, and version control.
- Partners with leadership on KPI frameworks and data strategy.
- Establishes RLS/CLS at scale; mentors and reviews others’ work.
- Optimizes cost/performance across the BI stack; drives automation.
Salary ranges (rough)
- Junior: ~$55k–$85k
- Mid-level: ~$85k–$120k
- Senior: ~$120k–$160k+
Varies by country/company; treat as rough ranges.
Skill map
Core areas you’ll master as a BI Developer:
- SQL: Query building, CTEs, window functions, incremental loads.
- Data Modeling for BI: Star/snowflake schemas, facts/dimensions, SCD.
- Dashboard Development: UX, chart selection, interactivity, accessibility.
- Performance Optimization: Query tuning, aggregation tables, caching.
- Data Security (Row/Column Level): RLS/CLS patterns and testing.
- Metadata Management: Catalogs, lineage, documentation, semantic layers.
- Source Control: Branching, PR reviews, CI checks for BI assets.
- Requirements Translation: Stakeholder interviews, metrics specs, scoping.
- DAX Calculation Logic (Generic concepts): Filters, evaluation context, measures.
- Data Governance Basics: Ownership, SLAs, PII handling, change management.
Mini task: From question to metric
Stakeholder asks: “Why is churn up?” Convert into:
- Metric: Monthly customer churn rate = churned_customers / active_customers_prior_month.
- Dimensional cuts: plan_tier, region, acquisition_channel.
- Time grain: monthly, last 18 months.
- Data quality checks: active_customers consistent across sources.
Learning path
- SQL fundamentals to advanced (1–3 weeks)
- Practice joins, window functions, and CTEs on real tables.
- Mini task: Write a query to find the top product by revenue per month.
- Data modeling for analytics (1–2 weeks)
- Design a star schema and document each dimension and fact.
- Mini task: Implement a simple Type 2 SCD for “Customer”.
- Dashboard development (1–2 weeks)
- Build a 1-page executive dashboard with drill-through.
- Mini task: Add a KPI card with a consistent definition tooltip.
- Performance tuning (1–2 weeks)
- Create aggregation tables and compare refresh times.
- Security and governance (1 week)
- Implement RLS by region and mask a PII column.
- Collaboration (ongoing)
- Use git for BI files, PRs for changes, and write a data dictionary.
Practical portfolio projects
- Executive Sales Dashboard: Star schema (Orders, Customers, Calendar), KPIs (Revenue, AOV, Conversion), RLS by region, and performance benchmarks.
- Marketing Funnel Analyzer: Cohort model, MQL→SQL→Won stages, time-to-convert metrics, and drilldowns by channel.
- Support SLA Monitor: Ticket fact table, SLA breach metrics, on-call alerts, and a daily refresh with incident report template.
- Inventory Health: Stock coverage days, reorder points, out-of-stock tracker; demonstrates slow-moving SKUs and seasonality.
- Finance Forecast View: Actuals vs forecast model, YTD variance, scenario slicers; documents metric definitions and ownership.
Quality checklist for any project
- Clear metric definitions and owner listed on the dashboard.
- Data model diagram with fact/dimension descriptions.
- Performance report: refresh time, largest tables, bottlenecks.
- Security notes: who sees what and how it’s enforced.
- Source control: link to commit history and PR notes (no credentials).
Interview preparation checklist
- Explain star vs snowflake and when to use each.
- Walk through a KPI definition, including edge cases and filters.
- Whiteboard an RLS rule and discuss testing strategy.
- Optimize a slow report: what to measure first and why.
- Translate a vague stakeholder request into a scoped plan.
- Talk about trade-offs: real-time vs batch, detail vs speed.
- Show version control workflow and rollback plan.
- Prepare stories of data incidents and how you fixed them.
Common mistakes (and how to avoid them)
- Undefined metrics: Always publish a definition with filters and grain.
- Over-normalized models: Prefer star schemas for analytics readability.
- Heavy visuals on weak models: Optimize data first, then visuals.
- No security tests: Add test accounts and explicit RLS/CLS test cases.
- Ignoring refresh windows: Communicate data latency and SLAs clearly.
- No version control: Use branches and PRs for all BI changes.
- Hidden PII: Mask or remove PII early; document who can see what.
- Siloed work: Pair with stakeholders regularly; validate early prototypes.
Mini task: RLS smoke test
Create two test users: “Regional_Manager_NA” and “Regional_Manager_EU”. Validate that each sees only their region’s data on all pages and exports.
Next steps
- Take the Fit Test below to gauge your alignment.
- Complete at least one portfolio project end-to-end.
- Then take the Core Exam. Everyone can take it; logged-in users get saved progress.
- Pick a skill to start in the Skills section below and set a 2–3 week learning sprint.
Pick a skill to start: open the Skills section below and choose the first topic.