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Being the Go-To Practitioner for Data Validation in High-Velocity Environments

$199.00
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A tailored course, built for your situation

Being the Go-To Practitioner for Data Validation in High-Velocity Environments

How to become the internal benchmark for trusted, repeatable data validation in fast-moving data ecosystems

$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.

Who this is for

Mid-senior individual contributor in data engineering or data quality, working hands-on with ETL testing and validation in cloud data platforms, seeking to increase technical authority and peer recognition

Who this is not for

This is not for managers looking for high-level oversight frameworks, or for beginners learning SQL or basic testing concepts. It’s for practitioners already doing the work and ready to own the standard.

What you walk away with

  • Recognition as the internal expert for data validation design in Snowflake environments
  • A personal library of reusable, peer-adopted validation patterns and check templates
  • Authority to define what ‘validated’ means across ingestion, transformation, and staging layers
  • Increased visibility from engineering leads when pipeline integrity is debated
  • Ability to mentor others using your documented, battle-tested validation workflows

The 12 modules (with all 144 chapters)

Module 1. The Shift from Testing to Trusted Validation
Understand how data validation is evolving from a QA step to a gatekeeping function in modern data stacks. Learn how top practitioners are defining the criteria for trusted data and securing recognition as the decision-makers.
12 chapters in this module
  1. From bug-finding to standard-setting
  2. Why validation now gates production
  3. The rise of self-serve validation
  4. How ICs gain authority in pipeline design
  5. Defining 'complete validation' clearly
  6. Recognising technical debt in test logic
  7. Mapping validation to stakeholder trust
  8. The validation ownership gap
  9. Building credibility through consistency
  10. How senior engineers delegate validation calls
  11. Creating a validation philosophy
  12. Benchmarking your approach internally
Module 2. Designing Reusable Validation Artefacts
Learn to create templates, checklists, and modular validation scripts that others adopt voluntarily. Turn one-off tests into shared assets that elevate your visibility.
12 chapters in this module
  1. Template vs script vs policy
  2. Naming conventions that stick
  3. Parameterising common checks
  4. Versioning validation logic
  5. Documenting assumptions clearly
  6. Designing for peer reuse
  7. Sharing via internal knowledge bases
  8. Packaging for Snowflake environments
  9. Using CTEs for test clarity
  10. Building validation building blocks
  11. Automating template deployment
  12. Tracking adoption across teams
Module 3. Validation Patterns for Complex Transformations
Master validation strategies for window functions, slowly changing dimensions, and incremental loads. Become the go-to person for hard-to-test logic.
12 chapters in this module
  1. Validating deduplication logic
  2. Testing time-series rollups
  3. Spotting drift in SCD Type 2
  4. Checking backfill accuracy
  5. Validating surrogate keys
  6. Testing merge statements
  7. Asserting referential integrity
  8. Monitoring row count variance
  9. Validating partition boundaries
  10. Testing null handling in joins
  11. Checking data type coercion
  12. Flagging unexpected truncation
Module 4. Establishing Peer Recognition
Learn how top ICs make their work visible, earn informal authority, and become the default reference point for data quality decisions.
12 chapters in this module
  1. When to speak up in design reviews
  2. Positioning feedback as standards
  3. Using Slack channels strategically
  4. Running lightweight brown bags
  5. Documenting decisions in RFCs
  6. Sharing validation post-mortems
  7. Asking the right review questions
  8. Gaining buy-in without authority
  9. Becoming the escalation point
  10. Owning the validation playbook
  11. Getting cited in PR descriptions
  12. Being mentioned in incident reports
Module 5. Defining Production-Ready Data
Move beyond passing tests, define what it means for data to be truly ready for downstream use. Lead the conversation with clarity and evidence.
12 chapters in this module
  1. Completeness thresholds
  2. Freshness SLAs by domain
  3. Accuracy sampling methods
  4. Schema stability criteria
  5. Documentation completeness
  6. Error budget for pipelines
  7. Downstream impact analysis
  8. Sign-off checklists
  9. Automated readiness gates
  10. Handling edge case exceptions
  11. Defining rollback conditions
  12. Communicating readiness status
Module 6. Validation in CI/CD Workflows
Embed validation into deployment pipelines so your standards are enforced automatically. Increase your impact without manual oversight.
12 chapters in this module
  1. Pre-merge validation checks
  2. Running tests in preview environments
  3. Fail-fast vs fail-late strategies
  4. Parallelising test execution
  5. Reporting test results clearly
  6. Integrating with dbt tests
  7. Using stored procedures for validation
  8. Alerting on test regressions
  9. Testing in staging before prod
  10. Validating DDL changes
  11. Handling test data provisioning
  12. Minimising false positives
Module 7. Metrics That Signal Trust
Track and communicate validation outcomes in ways that build confidence and visibility. Turn test results into proof of reliability.
12 chapters in this module
  1. Validation pass rate trends
  2. Test coverage by table
  3. Time to detect data issues
  4. Mean time to validate pipeline
  5. Reduction in downstream defects
  6. Peer citation frequency
  7. Adoption of your templates
  8. Number of escalations resolved
  9. Feedback loop speed
  10. Tickets prevented by validation
  11. Incidents attributed to missing checks
  12. Recognition in performance reviews
Module 8. Handling Edge Cases with Authority
Develop a systematic approach to rare but critical data anomalies. Become known for catching what others miss.
12 chapters in this module
  1. Identifying high-risk transformations
  2. Designing negative test cases
  3. Testing with synthetic edge data
  4. Validating timezone conversions
  5. Checking leap year logic
  6. Handling daylight saving shifts
  7. Testing locale-specific formats
  8. Validating currency conversions
  9. Spotting overflow in counters
  10. Testing for silent failures
  11. Logging unexpected values
  12. Creating anomaly playbooks
Module 9. Mentoring Through Documentation
Turn your expertise into teachable frameworks. Help others improve while reinforcing your status as the subject matter expert.
12 chapters in this module
  1. Writing beginner-friendly guides
  2. Creating validation decision trees
  3. Recording common anti-patterns
  4. Building annotated examples
  5. Using diagrams for logic flow
  6. Publishing test rationale
  7. Linking to real incidents
  8. Teaching via pull request comments
  9. Running internal workshops
  10. Answering questions publicly
  11. Curating a validation FAQ
  12. Updating docs with new cases
Module 10. Influencing Without Authority
Lead change by making your approach so clear and effective that others adopt it voluntarily. Build influence through consistency and results.
12 chapters in this module
  1. Proposing standards via RFC
  2. Piloting with one team first
  3. Showing before-and-after metrics
  4. Aligning with platform goals
  5. Using social proof in pitches
  6. Avoiding 'should' language
  7. Framing as team efficiency
  8. Highlighting risk reduction
  9. Gaining platform team endorsement
  10. Scaling through tooling
  11. Letting adoption grow organically
  12. Measuring influence qualitatively
Module 11. Owning the Validation Narrative
Shape how data quality is discussed in your organisation. Move from executing tests to defining what success looks like.
12 chapters in this module
  1. Reframing 'data issues' as process gaps
  2. Talking about trust, not bugs
  3. Using consistent terminology
  4. Defining validation maturity levels
  5. Benchmarking against peers
  6. Sharing quarterly validation reports
  7. Presenting at team retrospectives
  8. Influencing onboarding content
  9. Shaping job descriptions
  10. Defining promotion criteria
  11. Aligning with data governance
  12. Connecting to business outcomes
Module 12. Sustaining Recognition Over Time
Keep your validation frameworks current and relevant. Ensure your reputation grows alongside the data stack.
12 chapters in this module
  1. Reviewing templates quarterly
  2. Updating for new Snowflake features
  3. Retiring outdated checks
  4. Onboarding new contributors
  5. Soliciting feedback regularly
  6. Tracking tech debt in tests
  7. Adapting to new data sources
  8. Scaling patterns to new domains
  9. Measuring ongoing adoption
  10. Celebrating team wins
  11. Sharing lessons externally
  12. Continuing to raise the bar

How this maps to your situation

  • When joining a new data team with inconsistent practices
  • After a major incident caused by undetected data drift
  • During platform-wide migration to modern ELT
  • When asked to mentor junior engineers on testing

Before vs. after

Before
Validation work is seen as routine testing, done in isolation, with limited visibility beyond immediate tickets.
After
You're known as the standard-bearer for data integrity, your frameworks are reused across teams, and your opinion shapes what 'production-ready' means.

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: 45, 60 minutes per week for 12 weeks, with flexible pacing and downloadable resources for offline review.

How this compares to the alternatives

Unlike generic data quality courses that focus on theory or tooling, this program is built for ICs who want to be recognised for their technical judgment and practical frameworks. No other course maps validation work to peer recognition and informal authority in high-velocity environments.

Frequently asked

Is this course specific to Snowflake?
The principles apply to any cloud data platform, but examples and implementation guidance are tailored for Snowflake environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I get access to sample code?
Yes, every module includes downloadable SQL templates, validation check examples, and implementation playbooks you can adapt.
$199 one-time. 45, 60 minutes per week for 12 weeks, with flexible pacing and downloadable resources for offline review..

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
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