Data Quality Engineering and Automated Testing Training Course

 Data Quality Engineering and Automated Testing Training Course 

Introduction 

Data Quality Engineering and Automated Testing Training Course provides comprehensive practical knowledge and technical skills for designing, implementing, and managing enterprise data quality frameworks. The course explores modern data quality engineering, automated data validation, data profiling, data cleansing, data observability, test automation, and continuous data quality monitoring. Participants will learn how to leverage advanced technologies, artificial intelligence, machine learning, and automated testing frameworks to identify data anomalies, eliminate inconsistencies, improve data accuracy, and strengthen enterprise data governance. Emphasis is placed on developing scalable data quality pipelines, implementing automated testing strategies, and integrating quality assurance processes into modern data architectures, cloud data platforms, data warehouses, and data lakes. 

The training also examines industry best practices for implementing end-to-end data quality engineering across complex digital ecosystems. Participants will gain practical experience in developing automated data testing scripts, validating data transformation processes, monitoring data integrity, and implementing continuous integration and continuous deployment workflows. Through international case studies and practical exercises, learners will explore how organizations improve operational efficiency, regulatory compliance, business intelligence reliability, and decision-making through automated data quality assurance. The course equips professionals with actionable strategies for building reliable, scalable, and resilient data environments that support digital transformation and enterprise analytics. 

Course Objectives 

• Understand advanced data quality engineering principles and frameworks.
• Develop automated data validation and testing strategies.
• Apply data profiling and anomaly detection techniques.
• Implement automated data cleansing and transformation processes.
• Strengthen data integrity, consistency, and accuracy.
• Design scalable data quality testing pipelines.
• Apply continuous integration and continuous deployment practices.
• Utilize data observability and monitoring technologies.
• Identify data anomalies using machine learning techniques.
• Implement enterprise data quality management standards.
• Improve data governance and regulatory compliance.
• Automate data warehouse and pipeline testing.
• Optimize enterprise analytics through reliable data quality.

Organizational Benefits
 

• Improved enterprise data accuracy and reliability.
• Reduced operational costs through automated testing.
• Stronger regulatory compliance and data governance.
• Faster identification of data quality issues.
• Improved business intelligence and analytics outcomes.
• Increased productivity through automated workflows.
• Reduced risks associated with inaccurate information.
• Enhanced customer data management.
• Improved scalability of enterprise data infrastructure.
• Stronger organizational decision-making capabilities.

Target Audiences
 

1. Data Quality Engineers and Analysts.
2. Data Engineers and Database Administrators.
3. Business Intelligence and Analytics Professionals.
4. Data Architects and Enterprise Architects.
5. Software Quality Assurance Engineers.
6. Data Governance and Compliance Officers.
7. Information Technology Managers.
8. Machine Learning and Data Science Professionals.

Course Duration: 5 days

Course Modules

Module 1: Fundamentals of Data Quality Engineering
 

• Data quality dimensions, principles, and frameworks.
• Data accuracy, completeness, consistency, and validity.
• Enterprise data quality engineering architectures.
• Data quality lifecycle management.
• Quality requirements and performance indicators.
• Case study: Data quality improvement at Walmart.

Module 2: Data Profiling and Quality Assessment
 

• Automated data profiling methodologies.
• Statistical analysis and data distribution assessment.
• Missing values and duplicate record identification.
• Schema validation and metadata analysis.
• Data anomaly detection techniques.
• Case study: Customer data profiling in Amazon's retail operations.

Module 3: Automated Data Validation and Testing
 

• Automated data validation frameworks.
• Rule-based and constraint-based testing.
• SQL-based data quality testing.
• Referential integrity and consistency testing.
• Automated validation of structured datasets.
• Case study: Financial data validation in banking institutions.

Module 4: Data Cleansing and Transformation Testing
 

• Automated data cleansing techniques.
• Data standardization and normalization.
• ETL and ELT pipeline testing.
• Transformation validation and reconciliation.
• Error handling and data remediation.
• Case study: Enterprise data integration at Netflix.

Module 5: Data Quality Automation Tools and Technologies
 

• Introduction to Great Expectations and Soda.
• Automated testing using Python and SQL.
• Data quality workflows using dbt tests.
• Apache Airflow testing and orchestration.
• Integration of automated quality assurance tools.
• Case study: Automated data pipeline testing in modern cloud enterprises.

Module 6: Data Observability and Continuous Monitoring
 

• Data observability frameworks and architectures.
• Automated anomaly detection and alerting.
• Data freshness and pipeline reliability monitoring.
• Root cause analysis and incident management.
• Continuous data quality reporting.
• Case study: Data observability implementation in large-scale digital platforms.

Module 7: Continuous Integration and Deployment for Data Quality
 

• Integrating automated tests into development pipelines.
• Continuous integration and continuous deployment workflows.
• Version control and automated testing environments.
• Regression testing for data pipelines.
• Deployment validation and quality gates.
• Case study: Continuous testing practices in Microsoft Azure data environments.

Module 8: Enterprise Data Quality Governance and Capstone
 

• Enterprise data quality policies and standards.
• Data quality metrics, dashboards, and reporting.
• Risk management and regulatory compliance.
• Developing enterprise automated testing strategies.
• Capstone: Building an automated data quality testing framework.
• Case study: Enterprise data governance and quality management at IBM.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical exercises using real-world datasets.
• Hands-on automated testing and validation workshops.
• Group discussions and international case study analysis.
• Capstone projects and collaborative problem-solving.
• Practical demonstrations of industry-standard software tools.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.