Data Engineering For Managers

Training course

Overview

Data Engineering for Managers is a comprehensive professional training course designed to equip managers with the knowledge and management capabilities required to oversee modern data engineering initiatives effectively. The course provides a practical understanding of how data is collected, integrated, transformed, stored, governed, secured, and delivered for analytics, business intelligence, artificial intelligence, and organizational decision-making. Managers learn how to translate business requirements into data engineering priorities while coordinating people, technology, budgets, risks, quality expectations, and delivery objectives.

This Data Engineering for Managers course provides a management-oriented understanding of data architectures, data warehouses, data lakes, lakehouse platforms, ETL and ELT pipelines, batch and streaming processing, data modeling, cloud data platforms, orchestration, and data integration. Rather than focusing solely on programming, the course emphasizes how managers can evaluate technical proposals, define project scope, allocate resources, establish responsibilities, monitor delivery, and make informed decisions about data platforms and engineering approaches. Practical tools such as requirements matrices, roadmaps, RACI models, risk registers, architecture review checklists, KPI dashboards, and project governance frameworks are incorporated throughout the training.

The training also addresses the managerial responsibilities associated with data quality, governance, security, privacy, performance, reliability, cloud adoption, cost management, DevOps, DataOps, and operational support. Participants learn how to establish appropriate controls, define service expectations, monitor data engineering performance, manage incidents, evaluate technical debt, and support continuous improvement. Case studies, management exercises, and real-world scenarios help participants practice reviewing data engineering initiatives, identifying delivery risks, resolving cross-functional challenges, and communicating technical issues to business stakeholders and senior leadership.

By the end of this Data Engineering for Managers training course, participants will be able to effectively manage data engineering teams, projects, platforms, vendors, budgets, risks, and transformation initiatives. They will understand how to assess data engineering maturity, evaluate architecture and technology choices, establish governance and quality controls, manage performance and reliability, and align data engineering investments with organizational objectives. The course progresses from foundational management concepts through project and operational oversight to strategic data engineering leadership, making it suitable for managers responsible for data, technology, analytics, digital transformation, and enterprise information systems.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Data engineering managers and team managers

• IT managers and technology managers

• Data and analytics managers

• Business intelligence managers

• Database and data platform managers

• Project and program managers overseeing data initiatives

• Digital transformation managers

• IT operations and infrastructure managers

• Cloud and technology service managers

• Data governance and information management managers

• Software engineering managers working with data-intensive systems

• Managers responsible for data modernization and migration projects

• Technical leads transitioning into management roles

• Consultants and professionals responsible for managing enterprise data projects

Course Objectives

By the end of the training, participants will be able to:

• Explain the role, scope, lifecycle, and business value of data engineering

• Understand modern data engineering architectures and evaluate technology alternatives

• Translate organizational and analytical requirements into manageable data engineering initiatives

• Define project scope, deliverables, milestones, roles, responsibilities, and acceptance criteria

• Plan and coordinate data ingestion, integration, transformation, storage, and processing initiatives

• Manage ETL, ELT, batch, incremental, and streaming data engineering projects

• Evaluate data warehouses, data lakes, lakehouses, and cloud data platforms from a management perspective

• Establish data quality, governance, security, privacy, and compliance controls

• Manage data engineering teams, responsibilities, dependencies, vendors, and stakeholder expectations

• Monitor pipeline performance, platform reliability, service levels, quality, and operational KPIs

• Apply risk management, issue management, escalation, and incident management practices

• Manage data engineering budgets, resource allocation, cloud costs, and investment priorities

• Apply DataOps, DevOps, CI/CD, testing, automation, and continuous improvement principles

• Evaluate technical debt, modernization requirements, migration strategies, and platform lifecycle needs

• Establish effective documentation, reporting, governance, and architecture review processes

• Manage data engineering projects through implementation, deployment, and operational transition

• Communicate technical data engineering issues and decisions effectively to business and executive stakeholders

• Develop practical strategies for improving organizational data engineering capability and maturity

Course Content

Day 1: Data Engineering Foundations, Business Alignment, and Management Responsibilities

Module 1: Data Engineering Concepts, Architecture, Strategy, and Management Planning

Topics

  1. Introduction to Data Engineering Management, Business Value, Roles, Responsibilities, and the Data Lifecycle
  2. Data Engineering in Modern Organizations: Analytics, Business Intelligence, AI, Machine Learning, and Operational Decision-Making
  3. Data Sources and Data Environments: Databases, APIs, Applications, Files, Events, Warehouses, Lakes, and Lakehouses
  4. Modern Data Engineering Architecture: Ingestion, Storage, Processing, Transformation, Serving, and Consumption Layers
  5. ETL, ELT, Batch, Incremental, and Streaming Data Engineering Approaches
  6. Understanding Data Modeling, Data Integration, Pipelines, Orchestration, and Distributed Processing from a Management Perspective
  7. Business and Technical Requirements: Scope Definition, Prioritization, Business Rules, Deliverables, and Acceptance Criteria
  8. Data Engineering Project Planning: Work Breakdown Structures, Milestones, Dependencies, Resource Planning, and Delivery Roadmaps
  9. Management Tools and Frameworks: RACI Matrices, Requirements Matrices, RAID Logs, Stakeholder Maps, Architecture Review Checklists, and Project Dashboards
  10. Case Study and Management Exercise: Assess a Data Engineering Initiative and Develop an Initial Delivery and Governance Plan

Day 2: Data Engineering Projects, Teams, Quality, and Delivery Management

Module 2: Data Pipeline Management, Team Coordination, Quality, and Project Controls

Topics

  1. Managing Data Engineering Projects: Scope, Schedule, Resources, Dependencies, Deliverables, and Change Control
  2. Managing Data Ingestion and Integration Workflows Across Multiple Business and Technology Systems
  3. Managing ETL and ELT Development: Requirements, Source-to-Target Mapping, Transformations, Testing, and Deployment
  4. Data Modeling and Analytical Platform Management: Data Warehouses, Data Marts, Data Lakes, and Lakehouse Solutions
  5. Data Quality Management: Accuracy, Completeness, Consistency, Validity, Timeliness, Uniqueness, and Quality Expectations
  6. Data Pipeline Testing and Acceptance: Unit Testing, Integration Testing, Data Validation, Reconciliation, and Release Controls
  7. Managing Data Engineering Teams: Roles, Skills, Work Allocation, Collaboration, Performance, and Professional Development
  8. Managing Cross-Functional Dependencies: Analysts, Developers, Database Teams, Cloud Teams, Security, Governance, and Business Units
  9. Practical Management Tools: Sprint Boards, Kanban Boards, Action Logs, Issue Registers, Quality Checklists, Status Reports, and KPI Dashboards
  10. Real-World Exercise: Coordinate a Multi-Team Data Engineering Project from Requirements through Pipeline Delivery

Day 3: Performance, Security, Governance, Risk, and Operational Management

Module 3: Data Platform Performance, Security, Reliability, Governance, and Risk Management

Topics

  1. Data Engineering Performance Management: Pipeline Throughput, Processing Time, Latency, Resource Utilization, and Service Expectations
  2. Capacity Planning and Scalability: Workload Forecasting, Resource Allocation, Growth Planning, and Performance Management
  3. Data Platform Reliability: Availability, Resilience, Failure Recovery, Backup, Disaster Recovery, and Business Continuity
  4. Data Security Management: Authentication, Authorization, Role-Based Access, Encryption, Secrets Management, and Access Reviews
  5. Data Privacy and Protection: Sensitive Data Classification, Masking, Retention, Controlled Access, and Responsible Data Management
  6. Data Governance Management: Ownership, Stewardship, Policies, Standards, Metadata, Lineage, and Accountability
  7. Data Engineering Risk Management: Technical Risks, Operational Risks, Vendor Risks, Security Risks, Quality Risks, and Delivery Risks
  8. Incident and Problem Management: Monitoring, Escalation, Root-Cause Analysis, Corrective Actions, and Service Restoration
  9. Management Frameworks and Tools: Risk Registers, Control Matrices, SLA/SLO Tracking, Incident Logs, Audit Evidence, and Operational Dashboards
  10. Case Study and Scenario Exercise: Manage a Critical Data Platform Incident Involving Quality, Security, Performance, and Delivery Risks

Day 4: Cloud Data Engineering, Financial Management, Automation, and Transformation

Module 4: Cloud Platforms, Investment Management, DataOps, and Data Engineering Transformation

Topics

  1. Cloud Data Engineering Fundamentals: Cloud Storage, Compute, Networking, Managed Services, and Platform Architecture
  2. Managing Cloud Data Warehouses, Data Lakes, Lakehouses, Hybrid Environments, and Multi-Environment Deployments
  3. Cloud Migration Management: Assessment, Business Case, Migration Planning, Dependencies, Risks, and Transition Management
  4. Data Engineering Financial Management: Budgets, Resource Costs, Cloud Consumption, Cost Allocation, and Investment Controls
  5. FinOps Principles for Data Platforms: Cost Visibility, Usage Optimization, Forecasting, Accountability, and Continuous Cost Management
  6. DataOps and DevOps Management: Automation, Collaboration, Version Control, CI/CD, Testing, and Deployment Governance
  7. Managing Data Engineering Automation: Workflow Orchestration, Scheduling, Monitoring, Alerts, and Operational Controls
  8. Technology and Vendor Management: Platform Evaluation, Procurement Criteria, Service Agreements, Vendor Performance, and Exit Considerations
  9. Change Management and Organizational Adoption: Communication, Training, Stakeholder Engagement, Process Improvement, and Capability Development
  10. Real-World Case Study: Develop a Management Plan for Migrating a Legacy Data Platform to a Scalable Cloud-Based Architecture

Day 5: Strategic Data Engineering Management, Modernization, and Capstone

Module 5: Strategic Data Engineering Leadership, Continuous Improvement, and Management Capstone

Topics

  1. Strategic Data Engineering Management: Business Alignment, Operating Models, Capability Development, and Strategic Priorities
  2. Data Engineering Maturity Assessment: Current-State Analysis, Capability Gaps, Benchmarking, and Improvement Planning
  3. Enterprise Data Platform Lifecycle Management: Modernization, Technical Debt, Refactoring, Migration, and Platform Renewal
  4. Advanced Data Engineering Governance: Architecture Governance, Standards, Policies, Controls, Reviews, and Decision Rights
  5. Data Engineering KPIs and Management Dashboards: Reliability, Quality, Freshness, Delivery, Cost, Productivity, and Service Performance
  6. Managing Continuous Improvement: Root-Cause Analysis, Lessons Learned, Process Optimization, Automation, and Engineering Standards
  7. Strategic Workforce Management: Skills Planning, Team Structure, Capacity, Training, Succession, and Engineering Capability Development
  8. Executive and Stakeholder Communication: Data Engineering Reports, Business Cases, Risk Communication, Investment Proposals, and Decision Support
  9. Comprehensive Case Study: Evaluate an Enterprise Data Engineering Portfolio and Develop a Strategic Improvement Roadmap
  10. Management Capstone Exercise: Develop, Govern, Resource, Risk-Assess, Measure, and Present an End-to-End Data Engineering Management Strategy

 

Course Schedules:

Dates Fees Location Apply