dbt Analytics Engineering Bootcamp Certificate
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Bootcamp Mastery Certificate B40
Turn warehouse data into tested, documented and governed analytical products. Build a dbt project from models and sources through incremental transformations, tests, snapshots, macros, lineage, CI discipline and a reusable semantic metrics layer.
Benchmark: dbt learning videos and dbt Labs product sessions are publicly available video resources. Zuyini curates those materials and applies its own independent build-and-proof completion rubric; this does not represent a dbt Labs-issued certification or endorsement.
Core Video Sequence
01 — Analytics engineering + dbt project anatomy
02 — Models, refs + dependency DAGs
03 — Materializations: view, table + incremental
04 — Sources, seeds + snapshots
05 — Data tests + quality gates
06 — Documentation + lineage
07 — Jinja, macros + reusable transformation logic
08 — Performance + deployment discipline
09 — Governed metrics with dbt Semantic Layer
10 — Current production case: semantic consistency
Build-Along Checkpoints
- Checkpoint 1: Initialize a dbt project against a warehouse; define sources and staging models, use ref/source consistently and prove DAG-based build order.
- Checkpoint 2: Create intermediate and mart models with deliberate materializations; add one incremental model and document its unique key and backfill strategy.
- Checkpoint 3: Add generic and singular tests for keys, relationships, accepted values and one domain-specific business rule; make a failing test block promotion.
- Checkpoint 4: Use a snapshot or equivalent history pattern for a slowly changing entity; demonstrate how the project handles late or changed source records.
- Checkpoint 5: Create reusable Jinja/macros and generate documentation/lineage; enforce naming, descriptions, owners and one exposure or downstream consumer.
- Checkpoint 6: Define governed semantic metrics, then run a CI-style selective build/test workflow and quantify one performance/cost improvement.
7-Day Independent Capstone
- Day 1: Choose a real analytics domain; define business questions, grain, source contracts, metric definitions, data-quality expectations and target marts.
- Day 2: Build staging and intermediate models with clean refs/sources and tests; publish a reproducible DAG with documentation.
- Day 3: Create fact/dimension or equivalent marts plus one incremental model and one snapshot/history requirement.
- Day 4: Add reusable macros and tests that catch an injected source/business-rule defect; document root cause and fix.
- Day 5: Create a semantic model/metric layer for 3-5 governed KPIs and validate consistency across at least two query/BI use cases.
- Day 6: Add CI/deployment logic with selective builds, environment separation and artifacts; optimize one expensive model and capture evidence.
- Day 7: Publish repo, DAG/docs, data dictionary, test report, metric definitions, CI evidence, performance comparison, demo and analytics runbook.
dbt - Model, Test, Document & Govern Metrics
Ship an analytics engineering project that transforms raw warehouse data into trusted marts and governed metrics. Completion requires modular SQL, sources/contracts, tests, snapshots or incremental models, documentation/lineage, reusable macros and deployment evidence.
Proof Rubric — What Must Be Shown
- MODELING: Layered marts + explicit grain
- TESTING: Quality gates + failing-case proof
- HISTORY: Incremental/snapshot correctness
- MACROS: Reusable transformation logic
- LINEAGE: Docs, owners + downstream map
- METRICS: Semantic definitions + consistency
- CI/CD: Selective build/test promotion
- PERFORMANCE: Runtime/cost optimization