In an era where data underpins every engineering decision and drives digital transformation, ensuring its accuracy, consistency, and trustworthiness is no longer optional. Poor data quality and weak governance can lead to compliance breaches, costly errors, and compromised engineering outcomes. This course is designed to help organizations embed robust data quality and governance frameworks into their engineering and business ecosystems.
Participants will be introduced to the critical principles of data quality management covering dimensions such as accuracy, completeness, timeliness, and reliability. Through practical demonstrations, learners will gain insights into data profiling, cleansing, validation, and enrichment techniques, ensuring their engineering data pipelines deliver trustworthy outputs for mission-critical operations.
The course also emphasizes governance practices, regulatory integrity, and compliance requirements. Participants will learn how to establish policies, stewardship roles, and processes that align with international standards such as GDPR, HIPAA, ISO 8000, and industry-specific frameworks in energy, manufacturing, healthcare, and finance.
Practical labs will provide hands-on experience with governance and quality tools such as Collibra, Informatica, Talend, Apache Atlas, and cloud-native data governance platforms. Learners will design policies that safeguard data security, support ethical AI adoption, and ensure regulatory alignment across global jurisdictions.
Emerging topics such as data governance for AI/ML, responsible data stewardship, metadata-driven governance, and data lineage tracking will be covered to equip professionals for modern challenges. Participants will understand how governance intersects with data engineering, enabling compliance-ready data pipelines and automated quality checks.
By the end of this course, participants will be able to architect and implement governance-driven data engineering strategies that enhance decision-making, reduce risks, and ensure regulatory integrity positioning their organizations for sustainable, compliant, and innovation-ready operations.
Who Should Attend
- Data Engineers and Architects building compliance-ready pipelines.
- Data Governance Officers and Stewards responsible for policy enforcement.
- IT Managers overseeing enterprise-wide data governance strategies.
- Compliance Officers ensuring regulatory adherence across industries.
- Quality Assurance Professionals validating data integrity.
- Data Scientists requiring reliable and high-quality datasets.
- Business Intelligence Analysts working with sensitive enterprise data.
- Engineering Managers integrating governance frameworks in operations.
- Risk and Audit Professionals addressing data-related compliance risks.
- Cloud Engineers managing governance in hybrid and multi-cloud ecosystems.
Duration
10 days
Course Objectives
By the end of this course, participants will be able to:
- Understand core principles of data quality in engineering ecosystems.
- Define governance frameworks that ensure compliance and accountability.
- Implement data stewardship models for enterprise data ecosystems.
- Apply international regulatory standards (GDPR, HIPAA, ISO 8000).
- Use profiling, cleansing, and enrichment tools to improve data quality.
- Establish metadata management and lineage tracking systems.
- Govern data pipelines for ethical AI and ML applications.
- Design data governance strategies for hybrid and multi-cloud environments.
- Monitor and audit data quality across engineering workflows.
- Develop governance-driven policies for secure data sharing.
- Ensure compliance across industry-specific regulatory frameworks.
- Align governance practices with digital transformation initiatives.
Comprehensive Course Outline
Module 1: Introduction to Data Quality and Governance
- Importance of data quality in engineering decisions.
- Key governance principles and roles.
- Business value of trusted and compliant data.
- Data quality dimensions and their impact.
Module 2: Data Quality Fundamentals
- Accuracy, completeness, timeliness, and consistency.
- Tools for data profiling and validation.
- Data cleansing and enrichment techniques.
- Practical examples in engineering workflows.
Module 3: Governance Frameworks
- Defining data ownership and stewardship.
- Centralized vs. federated governance.
- Policy development and enforcement.
- Aligning governance with business objectives.
Module 4: Regulatory Compliance
- Overview of GDPR, HIPAA, ISO 8000.
- Industry-specific compliance (finance, healthcare, energy).
- Building auditable data processes.
- Risk management through governance.
Module 5: Metadata and Data Lineage
- Role of metadata in governance.
- Data catalogs for enterprise ecosystems.
- Tracking data lineage for transparency.
- Tools: Apache Atlas, Collibra.
Module 6: Data Governance Tools and Platforms
- Informatica Data Quality and Governance.
- Collibra for enterprise-wide governance.
- Talend for data quality automation.
- Cloud-native governance platforms.
Module 7: AI, ML, and Governance
- Ensuring ethical and responsible AI.
- Feature store governance for ML pipelines.
- Bias detection and fairness in data.
- Compliance in AI-driven decision-making.
Module 8: Governance in Cloud Environments
- Data quality in hybrid and multi-cloud setups.
- Governance strategies for AWS, Azure, GCP.
- Security, privacy, and compliance in cloud storage.
- Data residency and sovereignty challenges.
Module 9: Data Quality Monitoring
- Setting up KPIs and dashboards.
- Automating quality checks in pipelines.
- Error detection and correction strategies.
- Continuous improvement frameworks.
Module 10: Data Stewardship and Ownership
- Roles and responsibilities of data stewards.
- Cross-functional collaboration in governance.
- Empowering business units with data ownership.
- Building stewardship programs in enterprises.
Module 11: Governance for Engineering Systems
- Data governance in IoT and sensor data.
- Manufacturing and energy sector governance.
- Integrating governance into engineering pipelines.
- Ensuring data quality in real-time monitoring systems.
Module 12: Risk, Audit, and Compliance
- Audit frameworks for governance.
- Internal vs. external audits.
- Risk analysis and mitigation strategies.
- Tools for automated compliance reporting.
Module 13: Emerging Governance Models
- Data mesh and federated governance.
- Data fabric for integrated governance.
- Adaptive governance for fast-changing environments.
- Future of governance in engineering contexts.
Module 14: Security and Privacy in Governance
- Role-based access controls.
- Data encryption and anonymization.
- Secure data sharing policies.
- Balancing governance with innovation.
Module 15: Industry Case Studies
- Governance in financial engineering.
- Healthcare compliance with data governance.
- Energy and utilities sector examples.
- Manufacturing and industrial data quality.
Module 16: Project and Certification Preparation
- Designing an enterprise governance strategy.
- Implementing automated quality monitoring.
- Peer review and evaluation.
- Exam preparation for governance certifications.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.
Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment
Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
