Advanced Data Engineering for Data Management Professionals Training Course

Course Category : Data Management

This advanced programme equips data management professionals with the skills to engineer robust data pipelines, design scalable big data architectures, implement effective data governance, and optimise enterprise data platforms using modern data engineering tools and advanced SQL.
Duration: 5 Days – Level: Advanced

Introduction

As organisations increasingly rely on data to power analytics, AI, and critical decision-making, data engineering has become a cornerstone discipline for building reliable and scalable data platforms. Massive volumes of data must be captured, transformed, governed, and optimised within well-designed architectures that support both operational and analytical workloads.
This course provides a holistic view of enterprise-grade data engineering for data management professionals. Participants will learn how to design and orchestrate data pipelines, implement big data architectures, apply data governance and compliance controls, tune performance across data systems, and lead data engineering initiatives within their organisations. The programme emphasises practical patterns, architectural blueprints, and best practices applicable to real-world environments..

Targeted Audience

Data managers seeking deeper technical expertise in advanced data engineering
Senior data engineers looking to refine complex data engineering skills
IT professionals transitioning into data management and engineering roles
Data architects and database administrators expanding into modern data architectures
Technical leaders responsible for data strategy and enterprise data platforms

Targeted Skills

Advanced data pipeline design and orchestration
Big data architecture design and scalability
Data governance, security, and compliance implementation
Advanced SQL performance tuning and optimisation
Efficient resource management in large-scale data environments
Leadership and project management in data engineering initiatives

Expected Outcomes

Gain a deep understanding of data engineering’s role within enterprise data strategy
Design and implement advanced data pipelines for batch and real-time processing
Architect scalable big data solutions using distributed processing frameworks
Apply data governance, security, and compliance controls across data platforms
Optimise database structures and SQL queries for high-performance workloads
Establish monitoring and performance management practices for data systems
Lead and manage data engineering projects and cross-functional data teams
Develop a data strategy roadmap that aligns technology, governance, and performance

Training Topics Index

Core components of data pipelines. sources, transformations, sinks
Batch vs real-time processing. use cases, benefits, and trade-offs
Comparing ETL and ELT patterns in modern architectures
Orchestrating complex workflows with scheduling and pipeline tools (e.g., Airflow – conceptually)
Error handling, data quality checks, and resilience patterns in pipelines

Characteristics and challenges of big data (volume, velocity, variety)
Distributed data processing frameworks (Hadoop/Spark – conceptual overview)
Storage solutions. data lakes, warehouses, and hybrid architectures
Scalability strategies and performance optimisation in big data environments
Streaming data processing tools and patterns for real-time use cases

Definition and foundations of data governance
Components of a governance framework. policies, standards, roles, processes
Data cataloguing and lineage tracking in data engineering ecosystems
Access control, identity, and role-based permissions
Protecting sensitive data. privacy, encryption, and masking concepts

Principles of database and data platform performance tuning
Analysing and optimising SQL queries using execution plans
Indexing strategies and their impact on query performance
Managing compute, memory, and storage resources effectively
Implementing monitoring, alerting, and performance dashboards

Applying project management practices to data engineering initiatives
Leading and coordinating cross-functional data engineering teams
Designing and executing a holistic data strategy
Change management and stakeholder engagement in data projects
Staying current with evolving tools, frameworks, and data engineering trends

Course Features

Updated and Interactive Content
Hypothetical Examples and Case Studies
Pre- and Post-assessments to Measure Impact
Verified Certificate with a QR Verification Code

Advanced Data Engineering for Data Management Professionals Training Course