A tailored course, built for your situation
Being the go-to practitioner for clean, trusted data models at scale
How data engineers at leading cloud firms are becoming the default source of truth for analytics teams
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
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The situation this course is for
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Who this is for
Senior data engineer working in a cloud-native stack with DBT and Snowflake, focused on modeling consistency and transformation reliability
Who this is not for
Engineers focused only on pipeline ingestion or infrastructure setup without ownership of semantic models
What you walk away with
- Models consistently cited as the reference source by analytics and BI teams
- Clear ownership and naming conventions that prevent duplication across squads
- Documentation that stays accurate because it’s built into the modeling workflow
- Ability to demonstrate lineage from raw layer to business metric in under two minutes
- Recognition from cross-functional peers as the anchor point for metric consistency
The 12 modules (with all 144 chapters)
Module 1. Defining the canonical shape of core business entities
Establish single definitions for customer, order, and revenue that survive team turnover and schema changes.
12 chapters in this module
- Naming the core business entity
- Mapping source systems to unified definition
- Choosing the source of truth
- Documenting assumptions in-line
- Versioning ownership transitions
- Flagging probabilistic matches
- Codifying timezone rules
- Handling soft deletes
- Standardizing currency logic
- Embedding audit triggers
- Linking to upstream SLAs
- Publishing the first reference version
Module 2. Building transformation logic that others trust
Write DBT models with clarity so peers adopt them without asking for clarification.
12 chapters in this module
- Structuring model prefixes by domain
- Naming conventions for intermediate layers
- Adding purpose statements in headers
- Using tests to replace tribal knowledge
- Linking to business glossary terms
- Adding ownership metadata
- Flagging non-idempotent logic
- Isolating volatile vs stable logic
- Using exposures to declare usage intent
- Automating dependency checks
- Validating against prior period
- Signing off on production readiness
Module 3. Creating documentation that stays accurate
Move beyond outdated wikis to self-updating documentation embedded in the codebase.
12 chapters in this module
- Using description fields in YAML
- Auto-generating data dictionaries
- Embedding usage examples in docs
- Linking models to business owners
- Adding update triggers in PRs
- Versioning change notes
- Highlighting deprecation paths
- Including common misinterpretations
- Tagging regulatory implications
- Referencing upstream data quality
- Showing impact on downstream models
- Generating changelogs automatically
Module 4. Designing lineage that proves trust
Show how raw data becomes business metrics with unbroken, auditable paths.
12 chapters in this module
- Mapping raw to staging layer
- Adding column-level lineage
- Using DBT lineage graphs
- Validating transformation logic
- Timestamping pipeline stages
- Including owner approvals
- Flagging manual overrides
- Linking to data quality checks
- Showing null-handling rules
- Documenting fallback sources
- Preserving context across hops
- Exporting for compliance requests
Module 5. Standardizing metric definitions across teams
Prevent conflicting revenue or customer counts by establishing shared calculations.
12 chapters in this module
- Identifying high-conflict metrics
- Gathering stakeholder inputs
- Defining calculation logic
- Versioning metric formulas
- Publishing with clear ownership
- Adding usage policies
- Flagging edge cases
- Linking to reporting tools
- Handling currency conversions
- Auditing access requests
- Updating definitions transparently
- Archiving deprecated versions
Module 6. Structuring reusable data contracts
Create specifications teams commit to before building, reducing rework.
12 chapters in this module
- Defining schema expectations
- Setting SLA terms in code
- Adding owner sign-off fields
- Versioning contract iterations
- Publishing to internal registry
- Linking to monitoring alerts
- Including sample payloads
- Specifying error handling
- Automating compliance checks
- Requiring contract adherence
- Tracking adoption across teams
- Updating for regulatory changes
Module 7. Implementing ownership trails that hold
Make accountability visible even after team reshuffles or exits.
12 chapters in this module
- Assigning model owners in metadata
- Linking to org structure
- Adding escalation paths
- Setting backup owners
- Automating handover reminders
- Flagging orphaned models
- Including contact methods
- Tracking tenure periods
- Validating access permissions
- Auditing ownership changes
- Syncing with HR systems
- Publishing team-wide view
Module 8. Enforcing consistency across transformation layers
Ensure naming, formatting, and logic don't drift between squads.
12 chapters in this module
- Creating shared macros
- Standardizing date formats
- Enforcing timezone use
- Managing null representations
- Unifying customer ID formats
- Validating currency codes
- Applying naming linters
- Automating style checks
- Publishing style guide
- Requiring peer reviews
- Tracking policy adherence
- Updating for new standards
Module 9. Designing for peer reuse, not just delivery
Build models so others can adopt them without reinventing the wheel.
12 chapters in this module
- Adding usage examples
- Creating onboarding guides
- Including common query patterns
- Documenting limitations
- Flagging performance trade-offs
- Sharing known issues
- Providing test datasets
- Linking to related models
- Offering templates
- Collecting feedback loops
- Updating based on adoption
- Recognizing contributor input
Module 10. Responding to metric disputes with evidence
Settle disagreements fast using version-controlled definitions and lineage.
12 chapters in this module
- Identifying root cause of conflict
- Retrieving model version
- Showing input data snapshot
- Demonstrating transformation logic
- Highlighting test coverage
- Linking to ownership record
- Providing audit trail
- Comparing to prior periods
- Clarifying assumptions made
- Updating documentation post-resolution
- Flagging process improvements
- Preventing recurrence
Module 11. Scaling trust through automation and visibility
Make reliability self-evident through continuous checks and dashboards.
12 chapters in this module
- Scheduling health checks
- Alerting on schema drift
- Monitoring model freshness
- Tracking test pass rates
- Publishing uptime stats
- Creating trust dashboards
- Automating documentation sync
- Validating access controls
- Reporting on usage volume
- Highlighting peer citations
- Showing downstream impact
- Updating status publicly
Module 12. Becoming the recognized source of truth
Position your work so teams default to your models without prompting.
12 chapters in this module
- Publishing model registry
- Adding endorsement badges
- Sharing success stories
- Presenting at team syncs
- Writing internal blog posts
- Mentoring new hires
- Answering peer queries
- Tracking citation frequency
- Requesting feedback
- Updating based on demand
- Celebrating adoption milestones
- Handing off ownership
How this maps to your situation
- When launching a new data domain
- After a metric discrepancy arises
- During onboarding of new analytics staff
- Before a compliance audit cycle
Before vs. after
Before
Models get rebuilt in parallel because no one knows what’s trusted
After
Teams reference your work as the starting point for every new analysis
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 3 hours per module, with just-in-time applicability to live projects.
If nothing changes
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How this compares to the alternatives
Unlike generic data governance courses, this focuses on concrete modeling decisions, naming patterns, and documentation practices that create recognition through consistent, reusable outputs.
Frequently asked
Is this focused on DBT specifically?
Yes, all examples and templates are from live DBT-Snowflake implementations.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me get promoted?
It’s designed to make your impact visible and institutionalized, which positions you as a key contributor.
$199 one-time. Approximately 3 hours per module, with just-in-time applicability to live projects..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours