Data Governance & Data Quality — Skills Plan
Planned skill map for Data Governance & Data Quality. The curriculum will be developed and released in stages.
Define ownership, glossary terms, lineage, quality checks, contracts, access rules, and retention practices for reliable organizational data.
Potential roles: Data Governance Analyst · Data Quality Engineer · Data Steward · Metadata Engineer
Before you start
- SQL and data modeling basics
- Data lifecycle and business context
- Privacy and security fundamentals
What you’ll build
- Create a governed dataset with lineage
- Define quality contracts and remediation workflow
- Design a business glossary and ownership model
Prerequisites
PLANNED- SQL and data modeling basics
Build the SQL and data modeling basics knowledge needed to follow the Data Governance & Data Quality foundations.
- Data lifecycle and business context
Build the Data lifecycle and business context knowledge needed to follow the Data Governance & Data Quality foundations.
- Privacy and security fundamentals
Build the Privacy and security fundamentals knowledge needed to follow the Data Governance & Data Quality foundations.
Foundations
PLANNED- Data ownership and stewardship
Explain how Data ownership and stewardship works and where it fits in the Data Governance & Data Quality learning path.
- Business glossary and metadata
Explain how Business glossary and metadata works and where it fits in the Data Governance & Data Quality learning path.
- Data classification and lineage
Explain how Data classification and lineage works and where it fits in the Data Governance & Data Quality learning path.
- Quality dimensions and validation rules
Explain how Quality dimensions and validation rules works and where it fits in the Data Governance & Data Quality learning path.
Core Skills
PLANNED- Catalogs and metadata platforms
Use Catalogs and metadata platforms to implement a working capability and verify its behavior in Data Governance & Data Quality.
- Data contracts and schema evolution
Use Data contracts and schema evolution to implement a working capability and verify its behavior in Data Governance & Data Quality.
- Quality tests and issue management
Use Quality tests and issue management to implement a working capability and verify its behavior in Data Governance & Data Quality.
- Master and reference data concepts
Use Master and reference data concepts to implement a working capability and verify its behavior in Data Governance & Data Quality.
- Access policies and auditability
Use Access policies and auditability to implement a working capability and verify its behavior in Data Governance & Data Quality.
Advanced / Production
PLANNED- Federated and domain-oriented governance
Evaluate Federated and domain-oriented governance trade-offs against the performance, reliability, and safety needs of Data Governance & Data Quality systems.
- Lineage across batch and streaming systems
Evaluate Lineage across batch and streaming systems trade-offs against the performance, reliability, and safety needs of Data Governance & Data Quality systems.
- Retention, deletion, and lifecycle controls
Evaluate Retention, deletion, and lifecycle controls trade-offs against the performance, reliability, and safety needs of Data Governance & Data Quality systems.
- Governance metrics and operating models
Evaluate Governance metrics and operating models trade-offs against the performance, reliability, and safety needs of Data Governance & Data Quality systems.
- Responsible data and AI dataset practices
Evaluate Responsible data and AI dataset practices trade-offs against the performance, reliability, and safety needs of Data Governance & Data Quality systems.
Real-World Projects
PLANNED- Create a governed dataset with lineage
Deliver a working outcome with Create a governed dataset with lineage, then test it against the project requirements.
- Define quality contracts and remediation workflow
Deliver a working outcome with Define quality contracts and remediation workflow, then test it against the project requirements.
- Design a business glossary and ownership model
Deliver a working outcome with Design a business glossary and ownership model, then test it against the project requirements.
Career / Interview Preparation
PLANNED- Data quality incident walkthrough
Explain your approach to Data quality incident walkthrough, defend key trade-offs, and discuss what you learned.
- Governance operating model discussion
Explain your approach to Governance operating model discussion, defend key trade-offs, and discuss what you learned.
- Metadata and lineage portfolio review
Explain your approach to Metadata and lineage portfolio review, defend key trade-offs, and discuss what you learned.
Discussion
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