About This Course
As organisations increasingly rely on data pipelines, cloud analytics platforms, and large-scale data processing environments, the risk surface for cyber threats has expanded dramatically. Data engineers and analysts — who design, build, and query systems that hold an organisation's most valuable information assets — are now among the most critical lines of defence against data breaches, ransomware, insider threats, and regulatory non-compliance. Yet most data professionals receive little or no formal cybersecurity training, leaving critical gaps in how pipelines are secured, access is controlled, and sensitive data is handled.
The Cybersecurity for Data Engineers and Analysts Training programme is designed to close that gap. Delivered over ten intensive days, this course takes participants from foundational cybersecurity concepts through to advanced, role-specific security techniques covering cloud data environments, database security, API protection, encryption, identity and access management, and secure DevOps practices for data teams. Participants will work through realistic threat scenarios drawn from data engineering contexts, gaining hands-on experience with tools and frameworks used in modern security-conscious data organisations.
TrainingComplex has developed this curriculum in response to growing demand from organisations across Africa, the Middle East, and beyond for data professionals who can operate confidently at the intersection of data engineering and information security. Whether you are building ETL pipelines, managing data warehouses, running SQL analytics, or deploying machine learning models, this course will sharpen your ability to identify vulnerabilities, apply controls, and respond to incidents — all within the context of your existing technical role.
Participants leave the programme with a structured security mindset, practical toolkits, and the ability to engage meaningfully with their organisation's security teams and compliance functions. The course is equally valuable to individuals seeking personal professional development and to organisations looking to build a security-aware data engineering culture.
Who Should Attend
- Data Engineers responsible for designing and maintaining data pipelines and infrastructure
- Data Analysts who access, query, and report on sensitive or regulated datasets
- BI Developers and Analytics Engineers working with cloud or on-premises data warehouses
- Database Administrators seeking to strengthen their security posture
- Cloud Data Platform Engineers working with AWS, Azure, or Google Cloud data services
- MLOps and Data Science Engineers deploying models in production environments
- IT Managers overseeing data teams and wanting to embed a security-first culture
- Compliance and Risk Officers who work closely with data engineering functions
Learning Objectives
- Understand the cybersecurity threat landscape as it specifically applies to data engineering and analytics environments
- Identify and classify sensitive data assets within pipelines, warehouses, and analytics platforms
- Apply encryption techniques to data at rest, in transit, and in use across modern data stacks
- Design and enforce identity and access management controls for data systems and pipelines
- Secure cloud-based data platforms including data lakes, warehouses, and streaming services
- Detect and respond to threats targeting data infrastructure, including injection attacks, insider threats, and misconfiguration exploits
- Implement secure coding and pipeline development practices within CI/CD and DataOps workflows
- Conduct vulnerability assessments and security audits on data environments
- Ensure compliance with data protection regulations including GDPR, PDPA, and sector-specific frameworks
- Protect APIs, data-sharing endpoints, and inter-service communication used in data workflows
- Apply database security hardening techniques for SQL and NoSQL systems
- Develop and practise an incident response plan tailored to data environment breaches
- Monitor data environments using logging, alerting, and SIEM tools
Course Modules
Module 1: The Cybersecurity Landscape for Data Professionals
- Why data engineers and analysts are high-value targets for attackers
- Key terminology: threats, vulnerabilities, exploits, attack surfaces, and risk
- The data security threat landscape in 2025 and beyond
- How data breaches happen: pipeline compromises, misconfigurations, and insider threats
- Differences between general IT security and data-specific security concerns
- Overview of the CIA triad applied to data engineering contexts
Module 2: Data Classification and Sensitive Asset Management
- Frameworks for classifying data by sensitivity and regulatory status
- Identifying personal, financial, health, and proprietary data within pipelines
- Data inventory and asset mapping for engineering teams
- Labelling and tagging strategies in cloud data environments
- Data lineage tracking and its role in security governance
- Handling unstructured and semi-structured sensitive data in analytics workflows
Module 3: Identity and Access Management for Data Systems
- Principles of least privilege and need-to-know access in data environments
- Role-based access control (RBAC) and attribute-based access control (ABAC)
- Managing service accounts and pipeline credentials securely
- Multi-factor authentication and single sign-on for data platform access
- Secrets management tools: HashiCorp Vault, AWS Secrets Manager, and equivalents
- Auditing and reviewing access permissions regularly across data systems
Module 4: Encryption and Data Protection Techniques
- Encryption fundamentals: symmetric, asymmetric, and hashing algorithms
- Encrypting data at rest in databases, data lakes, and object storage
- Securing data in transit: TLS, mTLS, and secure API communication
- Column-level and row-level encryption in data warehouses
- Tokenisation, masking, and pseudonymisation for analytics use cases
- Key management lifecycle: generation, rotation, storage, and revocation
Module 5: Securing Cloud Data Platforms
- Security shared responsibility model across AWS, Azure, and Google Cloud
- Hardening cloud storage: S3 bucket policies, Azure Blob access controls, and GCS permissions
- Securing cloud data warehouses: Snowflake, BigQuery, and Redshift security configurations
- Virtual private clouds, network segmentation, and private endpoints for data services
- Cloud Security Posture Management (CSPM) tools and misconfiguration detection
- Audit logging and monitoring in cloud data environments using native and third-party tools
Module 6: Database Security and Hardening
- SQL injection attacks: mechanics, detection, and prevention in analytics queries
- Hardening configurations for PostgreSQL, MySQL, Microsoft SQL Server, and MongoDB
- Row-level security and dynamic data masking in relational databases
- Privilege escalation risks in database environments and how to mitigate them
- Secure backup and recovery procedures for database systems
- NoSQL security considerations for document, key-value, and graph databases
Module 7: Securing Data Pipelines and ETL Workflows
- Attack vectors specific to ETL pipelines: injection, man-in-the-middle, and data tampering
- Securing Apache Airflow, dbt, Spark, and Kafka deployments
- Validating and sanitising data inputs at ingestion points
- Immutable pipeline infrastructure and infrastructure-as-code security scanning
- Dependency and library vulnerability management in data engineering projects
- Pipeline integrity verification: checksums, hashing, and data quality security checks
Module 8: API Security and Data Sharing Endpoints
- Common API vulnerabilities affecting data platforms: OWASP API Top 10
- Authentication and authorisation for data APIs: OAuth 2.0, API keys, and JWT tokens
- Rate limiting, throttling, and abuse prevention for data-serving APIs
- Securing REST and GraphQL APIs used to expose analytics results
- Third-party data sharing: contractual controls and technical safeguards
- Monitoring API traffic for anomalous data access patterns
Module 9: Secure DataOps and DevSecOps Practices
- Embedding security into CI/CD pipelines for data engineering projects
- Static application security testing (SAST) for data pipeline code
- Container and Docker security for data workloads
- Infrastructure-as-code security scanning with tools like Checkov and Terraform Sentinel
- Secure version control practices: protecting repositories containing sensitive configurations
- Shift-left security culture: building security checkpoints into the data development lifecycle
Module 10: Compliance, Data Governance, and Regulatory Frameworks
- GDPR requirements relevant to data engineers: data minimisation, purpose limitation, and breach notification
- Africa-specific data protection laws: Kenya Data Protection Act, POPIA, and NDPR
- ISO 27001 and NIST Cybersecurity Framework applied to data environments
- Data retention, deletion, and the right to erasure in engineering practice
- Privacy by design principles for pipeline and analytics system architects
- Building a compliance evidence trail: documentation, logs, and audit artefacts
Module 11: Threat Detection, Monitoring, and SIEM for Data Environments
- Logging strategies for data pipelines, databases, and cloud data services
- Using SIEM platforms (Splunk, Microsoft Sentinel, Elastic SIEM) in data contexts
- Anomaly detection for data access: identifying unusual query volumes and exfiltration patterns
- Setting up alerts for critical data security events across infrastructure
- User and entity behaviour analytics (UEBA) for insider threat detection
- Building a security dashboard for data platform health and threat visibility
Module 12: Vulnerability Assessment and Penetration Testing for Data Infrastructure
- Conducting a structured security assessment of a data environment
- Penetration testing methodology applied to databases and data pipelines
- Using tools such as Nmap, SQLMap, and Metasploit in data security contexts
- Identifying and prioritising vulnerabilities using CVSS scoring
- Remediation planning: translating assessment findings into engineering actions
- Engaging with external penetration testers: scope definition and results interpretation
Module 13: Incident Response and Recovery for Data Breaches
- Building a data breach incident response plan aligned with regulatory requirements
- Detecting indicators of compromise in data infrastructure and pipeline logs
- Containment strategies for active pipeline compromises and database intrusions
- Forensic preservation of evidence from data environments without disrupting operations
- Communicating a data breach internally and externally: legal, regulatory, and reputational considerations
- Post-incident review: root cause analysis, lessons learned, and security improvement planning
Requirements & Prerequisites
Participants should have at least one to two years of practical experience working in a data engineering, data analysis, database administration, or closely related technical role. Familiarity with SQL, cloud platforms, or data pipeline tools such as Apache Spark, Airflow, dbt, or equivalent is expected. No prior formal cybersecurity training is required, though a basic understanding of networking concepts such as IP addresses, firewalls, and protocols will be helpful.
Training Methodology
- Threat modelling workshops where participants map real attack vectors against sample data pipeline architectures drawn from their own industries
- Hands-on lab sessions using cloud sandboxes to practice configuring encryption, IAM policies, and security group rules on live data environments
- Case study analysis of real-world data breaches — including pipeline compromises, cloud storage misconfigurations, and insider data exfiltration incidents
- Secure code review exercises where participants identify and remediate vulnerabilities in sample ETL scripts and data transformation code
- Capture-the-flag style challenges focused on data infrastructure attack and defence scenarios
- Group red-team versus blue-team simulations in which participants alternate between attacking and defending a mock data platform
- Compliance mapping exercises linking technical controls to GDPR, ISO 27001, and sector-specific data protection requirements
- Incident response tabletop exercises simulating a data breach from discovery through containment and post-incident review
- Peer-led presentations in which small groups research and present on an emerging threat or security tool relevant to data engineering
- Personal security action plan development, where each participant drafts a tailored roadmap for improving security in their own working environment
Organisational Benefits
- Reduced risk of data breaches originating from poorly secured pipelines, misconfigured storage, or weak access controls
- Improved regulatory compliance posture across data handling and processing workflows
- A data engineering team capable of self-assessing security risks without constant reliance on specialised cybersecurity staff
- Stronger governance over sensitive data assets including personal, financial, and health information
- Reduced remediation costs by embedding security earlier in the data development lifecycle
- Enhanced trust from clients, partners, and regulators in the organisation's data handling practices
- A security-aware engineering culture that integrates naturally with existing DevOps and DataOps processes
- Better incident response readiness, minimising downtime and reputational damage when threats materialise
Where Is Cybersecurity for Data Engineers and Analysts Training Offered?
This course runs in-person across: Abuja, Nairobi, Kisumu, Nakuru, Dubai, Arusha, Zanzibar, Addis Ababa, Pretoria, Kigali, Cape Town, Mombasa, Kampala, Accra, Naivasha, Lagos, Dar es Salaam, Johannesburg, Riyadh, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Live Virtual |
Online |
12 Oct 2026 – 23 Oct 2026 |
KES 160,000 |
| Live Virtual |
Online |
24 Oct 2026 – 13 Dec 2026 |
KES 160,000 |
| Abuja, Nigeria |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 5,600 |
| Nairobi, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 230,000 |
| Kisumu, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 240,000 |
| Nakuru, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 240,000 |
| Dubai, United Arab Emirates |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 8,200 |
| Arusha, Tanzania |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 4,200 |
| Zanzibar, Tanzania |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 5,000 |
| Addis Ababa, Ethiopia |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 4,800 |
| Pretoria, South Africa |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 6,600 |
| Abuja, Nigeria |
In-Person |
2 Nov 2026 – 13 Nov 2026 |
USD 5,600 |
| Zanzibar, Tanzania |
In-Person |
2 Nov 2026 – 11 Nov 2026 |
USD 5,000 |
| Nakuru, Kenya |
In-Person |
2 Nov 2026 – 11 Nov 2026 |
KES 240,000 |
| Dubai, United Arab Emirates |
In-Person |
2 Nov 2026 – 13 Nov 2026 |
USD 8,200 |
+227 more dates — view live schedule →
Frequently Asked Questions
Do I need a cybersecurity background to attend this course?
No prior cybersecurity qualification is required. The course is designed specifically for data professionals — engineers, analysts, and database administrators — who have solid technical data skills but limited formal security training. The curriculum builds from foundational concepts and quickly connects them to your everyday data engineering work.
What tools and platforms will we work with during the training?
Participants will gain hands-on experience with a range of industry-standard tools including AWS, Azure, and Google Cloud data security features, HashiCorp Vault, Apache Airflow security configurations, SQL and NoSQL database hardening utilities, SIEM platforms such as Splunk and Microsoft Sentinel, and infrastructure-as-code scanning tools like Checkov. All lab environments are provided — you do not need to bring pre-configured software.
Will I receive a certificate upon completion?
Yes. Participants who attend the full programme and complete the practical assignments receive a TrainingComplex Certificate of Completion in Cybersecurity for Data Engineers and Analysts. The certificate details the topics covered and the number of training hours, making it a useful credential for CPD records and professional profiles.
Is this course available online for remote participants?
Yes. TrainingComplex offers a Live Virtual delivery option for participants who cannot attend in person. The online format mirrors the in-person experience with live instruction, interactive lab sessions, group exercises, and real-time engagement with the trainer and fellow participants through a secure virtual classroom platform.
How is the course assessed — is there a formal exam?
There is no formal written examination. Assessment is practical and continuous: participants complete hands-on lab exercises, contribute to group security simulations, and submit a personal security action plan at the end of the programme. Trainers provide individual feedback throughout the course to ensure every participant meets the required standard for certification.
