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GEN2375 Mastering Data Platform and Analytics Engineering

$199.00
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The Executive Diagnostic and Governance Toolkit

Mastering Data Platform and Analytics Engineering

Score your own function red, amber or green, find out which part is weakest, and walk into the next budget round able to defend what you want to fix. Built for leaders reviewing Data platform and analytics engineering.

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

What you walk out with
A scored, ranked picture of your own function, and a defensible answer to what to fix first.
1 You stop guessing where you stand.
You finish with a score, not an opinion: every part of your function rated red, amber or green, with the weakest ranked first. Evidence: a Quick Scan for the shape of it, then seven domain assessments of 30 scored questions each, 210 in all, rolled into one scorecard, plus a maturity radar and a current-versus-target gap analysis.
2 You can defend the decision.
You walk into the budget round with the gap named, the owner named and done defined, instead of a case built on instinct. Evidence: project charter, scope statement, RACI, requirements traceability and work breakdown structure, pre-filled in your domain's language.
3 The work actually moves.
The month after the decision is already built, so nothing stalls waiting for someone to design a form. Evidence: more than 60 project templates across all five PMBOK process groups, plus runbooks, SOPs, a KPI framework, audit checklists and a risk matrix. 55 to 65 files in total.
4 You use it the day it lands.
No blank templates to interpret. Every workbook opens with what it is, who uses it, when, how, a 1 to 5 scoring guide, what good looks like, and a worked example you delete and type over.
The Quick Scan is one sitting. You will know your weakest area before the day is out.
Nothing in it is generic project management: the build rejects any file that could belong to another course. Updated after you enrol, so it reflects where the work stands now. The 144-chapter course is included behind it, for the parts you want to go deeper on.
Your data platform is expected to deliver insights instantly, but the work behind it is invisible, fragmented, and falling behind.

The situation this is built for

You are responsible for a function that must support real-time exploration, reliable transformation, and governed access — all while engineers struggle with undocumented pipelines, shifting stakeholder demands, and mounting technical debt. The pressure to deliver faster insights grows, but the core work of data modeling, pipeline orchestration, and testing remains under-resourced and misunderstood. You need a way to assess where your function truly stands and decide what to change — without depending on outside vendors or chasing trends.

Who this is for

Head of Data who owns the data platform and analytics engineering function, responsible for delivery, reliability, and team strategy.

Who this is not for

This is not for individual contributors looking to learn SQL or cloud tools, nor for executives seeking high-level data strategy without operational depth.

What you walk away with

  • Assess the maturity of your data platform across infrastructure, modeling, and governance
  • Diagnose root causes of slow analytics engineering velocity
  • Structure team rituals around data testing, documentation, and change control
  • Prioritize improvements that reduce technical debt and increase stakeholder trust
  • Build a roadmap that aligns engineering capacity with business demand

How this maps to your situation

  • Assessing current state of data platform maturity
  • Diagnosing root causes of delivery delays
  • Aligning team processes with stakeholder needs
  • Planning strategic improvements with real constraints

Before vs. after

Before
Unclear ownership, inconsistent practices, reactive fixes, and growing technical debt in the data platform and analytics engineering function.
After
A clear assessment of strengths and gaps, aligned team processes, documented decisions, and a roadmap for sustainable evolution.

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, designed to be completed at your pace over 6 to 12 weeks.

If nothing changes
Without a structured assessment, teams continue to deliver inconsistently, accumulate undetected technical debt, lose stakeholder trust, and fail to scale with business needs — risking both credibility and operational viability.

How this compares to the alternatives

Unlike generic data courses or vendor-specific certifications, this course focuses exclusively on the operational realities of owning and evolving a data platform and analytics engineering function — with no promotion of tools or external solutions.

Also included: the full course, for when you want the reasoning behind a finding (12 modules, 144 chapters)

Depth reference. The diagnostic and the templates stand on their own; this is what to read when you want the reasoning behind a finding.

Module 1. Understanding the Modern Data Platform
Define the components, boundaries, and expectations of a contemporary data platform in a data-driven organization.
12 chapters in this module
  1. Defining the scope of the data platform function
  2. Mapping stakeholders who depend on data outputs
  3. Identifying core responsibilities of platform ownership
  4. Differentiating between data engineering and analytics engineering
  5. Establishing ownership of data modeling standards
  6. Assessing the role of orchestration in pipeline design
  7. Clarifying responsibilities for data access controls
  8. Documenting expectations for data freshness and latency
  9. Evaluating team structure for platform maintenance
  10. Understanding dependencies on source systems and APIs
  11. Measuring platform stability through uptime metrics
  12. Tracking technical debt accumulation in transformation logic
Module 2. Data Modeling for Analytical Clarity
Examine how modeling choices impact query performance, maintainability, and business alignment.
12 chapters in this module
  1. Choosing between normalized and dimensional models
  2. Designing fact and dimension tables for scalability
  3. Implementing slowly changing dimension patterns
  4. Validating model assumptions with business teams
  5. Versioning data models across development cycles
  6. Balancing granularity with storage efficiency
  7. Enforcing naming conventions across models
  8. Documenting model lineage and business logic
  9. Testing model outputs against source data
  10. Refactoring models without breaking downstream usage
  11. Using tests to validate model accuracy over time
  12. Integrating model documentation into discovery tools
Module 3. Building Reliable Data Pipelines
Ensure data flows are resilient, observable, and maintainable across sources, transformations, and destinations.
12 chapters in this module
  1. Designing idempotent ingestion from source systems
  2. Handling schema drift in incoming data streams
  3. Implementing retry logic for failed pipeline runs
  4. Setting up alerting for pipeline failure detection
  5. Monitoring data volume and row count variance
  6. Validating data integrity after transformation
  7. Scheduling pipelines based on business needs
  8. Tracking pipeline run duration and performance
  9. Managing dependencies between pipeline stages
  10. Using checkpoints to isolate failure domains
  11. Auditing changes to pipeline configuration
  12. Creating rollback plans for pipeline updates
Module 4. Orchestration and Workflow Management
Structure complex workflows to run reliably and respond to changes in data availability and business rules.
12 chapters in this module
  1. Choosing between event-driven and time-based triggers
  2. Defining task dependencies in workflow design
  3. Using DAGs to represent data processing flows
  4. Configuring concurrency limits for task execution
  5. Setting up retry policies for transient failures
  6. Securing credentials used in workflow execution
  7. Logging task-level output for debugging
  8. Testing workflow logic before production deployment
  9. Versioning workflow definitions across environments
  10. Integrating workflow status into monitoring tools
  11. Scaling orchestrator infrastructure with demand
  12. Managing permissions for workflow modification
Module 5. Testing and Data Quality Assurance
Embed testing into the development lifecycle to prevent errors from reaching stakeholders.
12 chapters in this module
  1. Writing unit tests for data transformation logic
  2. Validating referential integrity across tables
  3. Checking for unexpected null values in key fields
  4. Monitoring distribution of numeric field values
  5. Setting thresholds for acceptable data drift
  6. Automating test execution in CI/CD pipelines
  7. Failing builds on critical test failures
  8. Documenting test coverage across data assets
  9. Prioritizing test creation for high-impact models
  10. Using statistical tests to detect anomalies
  11. Alerting on data quality rule violations
  12. Maintaining test suites as models evolve
Module 6. Governance and Access Control
Implement policies that ensure data is used appropriately while enabling access for innovation.
12 chapters in this module
  1. Classifying data by sensitivity and regulatory scope
  2. Defining roles for data access and modification
  3. Implementing row-level security policies
  4. Auditing access to sensitive data tables
  5. Managing personal data in compliance with regulations
  6. Documenting data retention and deletion policies
  7. Creating approval workflows for data sharing
  8. Enforcing encryption for data at rest and in transit
  9. Reviewing access logs for anomalous behavior
  10. Integrating governance checks into deployment pipelines
  11. Training teams on data stewardship responsibilities
  12. Tracking policy adherence across projects
Module 7. Documentation and Discovery
Make data assets understandable and trustworthy through consistent documentation and discoverability.
12 chapters in this module
  1. Automating documentation generation from code
  2. Maintaining data dictionary definitions over time
  3. Linking models to business KPIs and definitions
  4. Highlighting data freshness in discovery tools
  5. Indicating ownership and support contacts
  6. Versioning documentation alongside code changes
  7. Embedding usage examples in documentation
  8. Capturing data lineage from source to model
  9. Indexing documentation for searchability
  10. Using metadata to drive auto-tagging
  11. Validating documentation completeness in reviews
  12. Measuring adoption of documentation practices
Module 8. Analytics Engineering Workflows
Optimize how analytics engineers translate requirements into reliable, reusable data assets.
12 chapters in this module
  1. Structuring analytics engineer onboarding
  2. Defining standard processes for model requests
  3. Using pull requests for code review and feedback
  4. Integrating linting tools into development workflows
  5. Enforcing testing requirements before merge
  6. Managing branches for feature development
  7. Coordinating deployment schedules with stakeholders
  8. Tracking model usage after deployment
  9. Conducting retrospectives on delivery delays
  10. Balancing technical debt reduction with new work
  11. Measuring cycle time from request to delivery
  12. Aligning analytics engineering goals with roadmap
Module 9. Change Management and Deployment
Control how changes to data models and pipelines are introduced into production environments.
12 chapters in this module
  1. Establishing development, staging, and production environments
  2. Using version control for all data code
  3. Creating deployment checklists for reliability
  4. Automating deployment through CI/CD pipelines
  5. Rolling back changes after unexpected behavior
  6. Communicating changes to downstream consumers
  7. Scheduling deployments during low-impact windows
  8. Validating data consistency after deployment
  9. Managing schema changes without breaking queries
  10. Using feature flags to control model rollout
  11. Tracking deployment success and failure rates
  12. Auditing deployment history for compliance
Module 10. Performance and Cost Optimization
Balance query performance with infrastructure cost in cloud data environments.
12 chapters in this module
  1. Analyzing query execution plans for inefficiencies
  2. Indexing strategies for large fact tables
  3. Partitioning tables by time or category
  4. Caching frequently accessed datasets
  5. Right-sizing compute resources for workloads
  6. Monitoring query cost per user or team
  7. Setting budget alerts for data usage spikes
  8. Optimizing data formats for storage efficiency
  9. Archiving historical data to cold storage
  10. Using materialized views to speed access
  11. Evaluating cost of real-time versus batch
  12. Measuring ROI of performance improvements
Module 11. Stakeholder Collaboration and Feedback
Build effective feedback loops between data teams and business users.
12 chapters in this module
  1. Scheduling regular syncs with data consumers
  2. Creating templates for data request intake
  3. Prioritizing requests based on business impact
  4. Demonstrating new models to stakeholders
  5. Collecting feedback on data usability
  6. Tracking resolution of reported data issues
  7. Managing expectations around delivery timelines
  8. Translating business questions into data models
  9. Documenting decisions made during requirement sessions
  10. Sharing data health dashboards with teams
  11. Involving stakeholders in data quality reviews
  12. Measuring stakeholder satisfaction over time
Module 12. Roadmapping and Strategic Evolution
Translate assessment findings into a prioritized, executable plan for platform improvement.
12 chapters in this module
  1. Conducting a maturity self-assessment across domains
  2. Identifying critical gaps in current capabilities
  3. Benchmarking against industry patterns and practices
  4. Engaging leadership in strategic direction setting
  5. Defining measurable goals for platform evolution
  6. Prioritizing initiatives based on effort and impact
  7. Sequencing improvements to build momentum
  8. Allocating team capacity to strategic work
  9. Communicating roadmap progress transparently
  10. Revisiting assumptions based on new constraints
  11. Adjusting roadmap in response to feedback
  12. Documenting decisions made during planning sessions

Frequently asked

Who is this course designed for?
This course is for Heads of Data who own the data platform and analytics engineering function and need to assess its maturity and plan improvements.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there hands-on coding or tool-specific training?
No. The course focuses on decisions, processes, and team practices — not syntax or specific vendor tools.
What deliverables come with the course?
Each module includes downloadable templates and examples, plus a hand-built implementation playbook delivered at enrollment.
Can I access the course materials after completion?
Yes. You retain access to all course content and updates for one year.
What formats do the templates come in?
The implementation playbook downloads as PDF and editable XLSX. The course reads in your learning environment and exports to PDF for offline use. The files are yours to keep.
Can I share this with my team?
The licence is per person. Team pricing opens from three seats: reply to the order confirmation with TEAM and we will set it up.
How quickly can I start?
The diagnostic is one sitting and the templates work straight out of the kit. Account access takes up to 24 hours rather than being instant, because every order is checked and updated against the latest sources before it is delivered.
$199 one-time. Approximately 3 hours per module, designed to be completed at your pace over 6 to 12 weeks..

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·Know your weakest area today·210 scored questions·Course included· Account access within 24 hours
30-day money-back guarantee, no questions asked.
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