About the Course
The core challenge in today's data-driven environment is not just collecting data, but ensuring its availability, integrity, and flow across the enterprise. Organizations require results they can prove through verifiable data lineage and automated governance. To achieve this, you need to master five critical capabilities: schema design for cloud warehouses, complex SQL transformation logic, Python-based pipeline scripting, workflow orchestration, and automated data quality testing. This course provides a structured system to turn scattered data sources into a unified, high-availability data lakehouse architecture using the Medallion Architecture pattern.
You will gain specific capabilities in building idempotent data pipelines, managing state in distributed systems, and implementing CI/CD for data infrastructure. You will practice hands-on deployment of Apache Airflow for orchestration and dbt for modular SQL modeling, while being introduced to the broader ecosystem of Apache Kafka for streaming and Kubernetes for containerized data tasks. This course is designed for professionals who must deliver high-uptime data services under the constraints of limited engineering resources and strict regulatory compliance requirements. Data Engineering and Workflow Automation Training is the definitive method for professionals to operationalize data at scale. It involves the integration of software engineering best practices into the data lifecycle. Professionals use it to reduce manual intervention, eliminate data downtime, and accelerate the time-to-insight for business intelligence teams.
Target Audience
This program is tailored for technical professionals responsible for the architecture, reliability, and automation of enterprise data assets.
This course is designed for:
- Data Engineers responsible for building and maintaining scalable ETL/ELT pipelines
- Analytics Engineering Leads overseeing modular SQL modeling and data transformation logic
- ETL Developers transitioning from legacy on-premise tools to cloud-native automation
- BI Architects designing the integration layer between data warehouses and visualization tools
- Data Warehouse Administrators optimizing performance for Snowflake, BigQuery, or Redshift
- Cloud Infrastructure Engineers supporting data platform deployments on AWS, Azure, or GCP
- Data Quality Analysts implementing automated testing frameworks and observability dashboards
- Platform Engineers managing containerized data workloads using Docker and Kubernetes
- Machine Learning Engineers building automated feature engineering pipelines for production models
- Software Developers moving into data-centric roles requiring workflow orchestration expertise
Course Objectives
This course equips you to design, execute, and report data engineering initiatives that ensure high availability, regulatory compliance, and strategic scalability.
By the end of this course, you'll be able to:
- Construct scalable data architectures using the Medallion Architecture and Data Lakehouse patterns
- Apply advanced SQL window functions and CTEs to solve complex data transformation challenges
- Develop automated workflow DAGs in Apache Airflow to orchestrate multi-stage data pipelines
- Implement modular data modeling using dbt to ensure version-controlled and tested SQL code
- Execute data quality audits using Great Expectations to identify and mitigate data drift
- Navigate cloud-native data storage strategies across Snowflake, BigQuery, and Amazon S3 environments
- Measure pipeline performance using custom KPI dashboards and automated observability alerts
- Synthesize data engineering workflows with CI/CD practices using Git and automated deployment runners
Requirements & Prerequisites
Participants should have a foundational knowledge of SQL (joins, aggregations) and basic Python programming. Experience with any relational database or cloud platform is recommended but not required. A laptop with Docker Desktop installed is necessary for hands-on exercises.
Professional and Organizational Impact
When you lead data engineering with credible automation and practical strategies, you become a trusted driver of operational agility and analytical precision.
As a professional, you will benefit by:
- Build technical expertise in modern orchestration tools like Apache Airflow
- Gain decision-making confidence using data observability and quality metrics
- Strengthen your professional positioning as a high-demand cloud data specialist
- Enhance your ability to balance pipeline performance with infrastructure costs
- Develop production-grade coding skills in Python and advanced SQL
- Position yourself for leadership roles in analytics engineering and architecture
- Expand your career reach across global, cloud-first technology organizations
Organizations that embed data engineering excellence into their operational context reduce costs, mitigate risks, and build lasting competitive advantage.
Your organization will benefit from:
- Reduced operational costs through the automation of manual data preparation tasks
- Mitigated compliance risks by implementing automated data lineage and governance
- Improved market positioning through faster delivery of real-time business insights
- Enhanced data reliability ensuring executive decisions are based on accurate information
- Scalable infrastructure capable of handling exponential growth in data volume
- Increased engineering productivity through modular code reuse and CI/CD workflows
- Minimized data downtime via proactive observability and automated error recovery
Training Methodology
This is a practical, outcome-driven course designed to turn data engineering aspirations into measurable action and credible reporting.
Methodology includes:
- Hands-on pipeline construction using real-world datasets and cloud-native SQL dialects
- Scenario simulation requiring incident response to pipeline failures and data quality breaches
- Data architecture diagnostic using the Well-Architected Framework for data-intensive applications
- Stakeholder reporting exercise focused on communicating data uptime and pipeline health metrics
- Case study analysis from the financial services, e-commerce, and healthcare sectors
- Group workshop producing a functional dbt project with documentation and testing
- Reflection exercise benchmarking current data practices against the Data Mesh maturity model
Upcoming Sessions
Next available dates worldwide
Certification
Recognized credentials that advance your career
Participants who complete the Data Engineering and Workflow Automation Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.
NITA Accredited
Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.
CPD Certified
Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.
Why this course earns its place on your CV
Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.
Skills Relevance
- Master cutting-edge tools for big data management and automation.
- Learn to build scalable data pipelines that power modern businesses.
- Transform raw data into actionable insights with advanced analytics techniques.
Expert Delivery
- Courses taught by seasoned data engineers from leading tech companies.
- Gain exclusive industry insights through real-world case studies and examples.
- Direct mentorship opportunities to guide your learning journey and project work.
Career Advancement
- Enhance your resume with skills in high demand by top tech employers.
- Prepare for roles like Data Architect and Automation Engineer with confidence.
- Access to a professional network of peers and industry leaders.
