Description

A focused course, tailored for you

Building an Analytics-Engineer Practice for Consulting Engagements

Build an analytics-engineer practice from scratch in 12 weeks. dbt + semantic-layer + governance + delivery model + pricing.

Analytics engineering is now a distinct discipline distinct from data engineering and BI. Consulting clients ask for analytics-engineer support by name. Firms without a productised analytics-engineer practice lose engagements to specialist firms. Here's the 12-week build.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

Analytics engineering became a distinct discipline through dbt, the semantic layer (Cube, dbt semantic layer, MetricFlow, Looker Universal Semantic Model), and the shift from raw-data engineering toward modelled-data product delivery. Consulting clients now ask for analytics-engineer support by name and expect productised delivery: dbt project standards, semantic-layer adoption, documentation as code, testing rigor, governance integration, and the analytics-engineer career architecture.

This course teaches the 12-week analytics-engineer practice build: dbt project standards, semantic-layer selection and integration, documentation-as-code, testing strategy, governance integration, delivery model, pricing, and the talent model. Twelve modules, each ending with a deliverable artefact. Plus a hand-built implementation playbook for your specific practice.

What you walk away with

A documented dbt project standard.
A semantic-layer selection and integration pattern.
A documentation-as-code workflow.
A testing strategy (data tests, unit tests, freshness, source).
A governance integration framework.
A delivery model for analytics-engineer engagements.
A pricing model.
A 12-week practice build plan.

The 12 modules

Module 1. Analytics-engineer practice landscape

Detailed walkthrough of analytics-engineering as discipline, dbt ecosystem (Core OSS, Cloud, Mesh), semantic-layer landscape (MetricFlow, Cube, Looker LookML/USM, AtScale), the relationship to data-engineering and BI, the talent market, and the client-demand signal patterns. When to position analytics-engineer support distinct from data-engineer or BI work. Three worked examples of analytics-engineer engagements at peer firms.

Module 2. dbt project standards

Build the dbt project standard: directory structure (staging, intermediate, marts, snapshots), naming conventions, tagging conventions, materialisations strategy (view, table, incremental, ephemeral), source-system declarations, exposure declarations, and the project documentation pattern. Deliverable: dbt project standard. Three worked examples drawn from real implementation packages plus the conversation-script for the next sponsor meeting.

Module 3. Semantic-layer selection and integration

Build the semantic-layer selection: dbt semantic layer (MetricFlow), Cube, Looker LookML and Universal Semantic Model, AtScale. Integration with dbt models, metric definition convention, downstream BI consumer pattern, and the versioning model. Three semantic-layer patterns from peer engagements. Deliverable: semantic-layer integration document.

Module 4. Documentation-as-code workflow

Build the documentation-as-code workflow: dbt docs structure, column-level descriptions, model-level descriptions, source descriptions, exposure descriptions, lineage rendering, custom documentation themes, and the documentation-publication pattern (dbt Cloud, GitHub Pages, hosted). Documentation is the analytics-engineer product. Deliverable: documentation workflow. Three worked examples drawn from real implementation packages plus the conversation-script for the next sponsor meeting.

Module 5. Testing strategy

Build the testing strategy: dbt tests (uniqueness, not-null, accepted-values, relationships, freshness, source), custom tests, generic tests, unit tests (dbt unit testing), test selection strategy, test orchestration, and the test-coverage targets. The testing rigour that distinguishes analytics-engineer work. Deliverable: testing strategy document.

Module 6. Governance integration

Build the governance integration: catalog integration (dbt + Unity + Polaris + Atlan + Datahub + Alation), data-contract patterns, PII tagging, sensitive-data handling, audit logging, and the cross-team data-contract enforcement. Governance is what makes analytics-engineering production-grade. Deliverable: governance integration framework.

Module 7. CI/CD and orchestration

Build the CI/CD and orchestration: dbt-cloud-CI patterns, GitHub Actions / GitLab CI integration, deferred-state-based testing, slim-CI, production-environment promotion, orchestration tool selection (Airflow, Dagster, Prefect, dbt Cloud scheduler), and the failure-handling model. Deliverable: CI/CD and orchestration document. Three worked examples drawn from real implementation packages plus the conversation-script for the next sponsor meeting.

Module 8. Analytics-engineer delivery model

Build the delivery model: engagement sprint structure, kickoff playbook, discovery-and-scope template, build-sprint deliverables, integration-sprint deliverables, handover and training, and the post-engagement support model. The delivery model that compresses cycle time. Deliverable: delivery model document. Three worked examples drawn from real implementation packages plus the conversation-script for the next sponsor meeting.

Module 9. Pricing model

Build the pricing model: fixed-fee project, retainer (steady-state support), outcome-based (metric-development KPI), team augmentation (per-resource), and the hybrid model. Pricing-power analysis: willingness-to-pay, cost-to-deliver, competitive benchmarking. Deliverable: pricing model document with three options. Three worked examples drawn from real implementation packages plus the conversation-script for the next sponsor meeting.

Module 10. Talent and career architecture

Build the analytics-engineer talent model: career architecture (junior, senior, lead, principal), promotion criteria, capability development, compensation alignment, and the recruitment-and-retention pattern in the 2026 talent market. The career architecture that retains specialised talent. Deliverable: career architecture document. Three worked examples drawn from real implementation packages plus the conversation-script for the next sponsor meeting.

Module 11. Sales enablement

Build the sales enablement: positioning statement, demo (showing a dbt project in 15 minutes), ROI calculator, case studies (3 minimum), competitive battlecards, and the discovery-conversation guide. Sales enablement is what makes the analytics-engineer practice sellable. Deliverable: sales enablement pack. Three worked examples drawn from real implementation packages plus the conversation-script for the next sponsor meeting.

Module 12. Your 12-week practice build plan

Week-by-week plan with weekly deliverables. Weeks 1-2: practice landscape + dbt project standard. Weeks 3-4: semantic-layer selection + documentation workflow. Weeks 5-6: testing strategy + governance integration. Weeks 7-8: CI/CD + orchestration. Weeks 9-10: delivery model + pricing model. Weeks 11-12: talent model + sales enablement + practice launch. Deliverable: full practice pack.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Modules 1 to 7 cover the technical productisation (dbt standard, semantic layer, documentation, testing, governance, CI/CD).

Modules 8 to 11 cover delivery model, pricing, talent, and sales enablement.

Module 12 covers the 12-week practice build plan.

What you get with this course

The 12-module course delivered as text plus downloadable templates.
Templates for dbt project standard, semantic-layer integration, documentation workflow, testing strategy, governance integration, CI/CD orchestration, delivery model, pricing, career architecture, sales enablement.
A hand-built implementation playbook generated for your specific practice.
Three worked examples of analytics-engineer practices at peer consulting firms.
Scripted talking points for practice principal pitch.

What you will have in hand by Day 1, Week 1, Month 1

Day 1: Practice landscape diagnostic completed.

Week 4: dbt project standard + semantic-layer integration delivered.

Week 8: Governance + CI/CD operational.

Week 12: Practice launched with first engagement.

Before and after

Before

Your firm ships data engagements. Analytics-engineering is mixed in with data-engineering. Clients ask for dbt support but no productised offering exists.

After

A productised analytics-engineer practice is in place. dbt project standard, semantic layer, documentation, testing, governance, CI/CD, delivery model, pricing, and talent architecture are all designed. Sales is selling first engagements.

What happens if you do not address this

Analytics-engineering demand is growing. Firms without a productised practice lose engagements to specialist firms (dbt Labs, Brooklyn Data, Data Folk, Mountain).

Who it is for

For consulting data engineers, analytics engineers, practice leaders, and capability leads building productised analytics-engineer offerings.

Who this is NOT for. Pure research roles. Firms not building data engagements.

How it arrives

Text-based course via LMS, plus downloadable templates and the hand-built implementation playbook.

Time investment. Roughly 16 hours of reading and 100 to 200 hours of team effort across the 12-week build.

Why $199 is the right number

External analytics-engineering consultants charge $200K-$1M for engagements. Specialist firms (dbt Labs, Brooklyn Data, Mountain, Data Folk) charge $300K-$1.5M. $199 buys the focused playbook plus the implementation document for your specific practice.

FAQ

Will this replace hiring an analytics-engineering consultant?

Partially. It teaches you the practice build. You may still want specialist input for novel semantic-layer integration.

What if my firm is Databricks-anchored (vs Snowflake)?

Modules 2 + 3 cover both anchored patterns.

Does this cover dbt Mesh and cross-project governance?

Module 6 covers dbt Mesh patterns.

What about reverse-ETL and operational analytics?

Module 1 covers reverse-ETL as adjacent pattern.

What is in the implementation playbook for me specifically?

A practice landscape diagnostic; dbt project standard tailored to your client tech-stack; a 12-week build plan.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.