Business Intelligence Engineer
Open a video or resource link, watch it, then check its box to track your progress and unlock what comes next.
- Build trustworthy analytical foundations with strong SQL and data-model design
- Own reliable pipelines, governed metrics and self-service BI products
- Lead experimentation, governance and AI-native analytics architecture at scale
Benchmark: AWS/Amazon BIE + Apple BI/data-product roles (current Sep 2026) Mastery ladder: 2 current-market competencies per level; 2 substantive learning bridges per competency (12 total).
Search Live Openings
We don’t have a direct job-board feed matched to this one yet, so here are real, live US job searches for “Business Intelligence Engineer” instead.
Build trustworthy analytical foundations with strong SQL and data-model design
Advanced SQL querying, views, CTEs, partitioning and secure access
Coverage: SQL & Analytics / Databases / Data Engineering
Data modeling, schemas and scalable analytical foundations
Coverage: Data Modeling, Warehousing & Lakehouse / BI Engineering
Own reliable pipelines, governed metrics and self-service BI products
ETL/ELT pipelines, orchestration, quality and scalable data products
Coverage: Data Engineering / Data Pipelines / Data Quality
Semantic metrics, dashboards and governed self-service analytics
Coverage: Semantic Layer / Product Metrics / BI & Visualization
Lead experimentation, governance and AI-native analytics architecture at scale
Experimentation, causal inference, anomaly detection and decision science
Coverage: Experimentation / Statistics / Causal Inference
Governance, lineage, agentic BI and AI-ready analytics architecture
Coverage: Data Governance & Lineage / AI Search & Agents / BI Architecture
Role-Readiness Proof / Capstone
Build and operate a production-grade BI product for a real or simulated business domain: define source-of-truth KPIs and metric contracts; model an analytical warehouse/lakehouse; build SQL/Python ETL with validation, observability and lineage; publish a governed semantic layer and self-service dashboard; implement role-based access and documentation; analyze one experiment or causal question plus anomaly detection; integrate an AI/agentic BI experience over governed data; then present an executive decision narrative and a runbook for quality, refresh failures and metric changes.
Proof Rubric
- Show expert SQL and model design
- reliable, replayable pipelines
- trusted metric semantics and reconciliation
- automated quality tests and lineage
- secure governed self-service
- dashboard clarity and stakeholder adoption
- statistically valid experiment or causal reasoning with uncertainty
- root-cause analysis of anomalies
- responsible AI-grounding and access controls
- operational runbook evidence
- and measurable decision or productivity impact