Architect the platform
Design a lakehouse-based enterprise platform with curated layers and canonical models.
Architecture Path · Platform & Integration · Step 7B
Enterprise Data Platform & Integration
The program for the leader who builds and runs the enterprise data platform: lakehouse and canonical models, contract-first integration, governance and MDM, AI-ready data products, observability, and secure platform operations. It treats data as a reliable internal product, mapped to the Sr. Director of Enterprise Data Platform, Integration and Architecture role reporting to the CIO.
8
Modules
40
Chapters
Sr. Director
Level
Capstone
Platform + integration blueprint
The journey
Eight modules, one arc
The eight modules build cumulatively toward a real capstone. Watch the work move, and the value compound, at every stage.
Each module builds the capability the next one depends on, ending in a portfolio-ready capstone.
Outcomes
What you'll be able to do
Integrate reliably
Build contract-first integration with APIs, streaming, and batch that is resilient and observable.
Govern the data
Establish governance, MDM, quality, metadata, lineage, and classification as a framework.
Enable AI systems
Deliver AI-ready data products for RAG, GenAI, and agentic workflows, safely.
Operate with reliability
Run platforms for reliability, performance, monitoring, and incident response.
Secure and lead
Ensure access control, privacy, and compliance, and lead teams, vendors, and adoption.
Curriculum
8 modules, 40 chapters, ending in a capstone
Eight modules of five chapters each, sequenced so the material builds cumulatively to a real, portfolio-ready capstone. Expand any module for its focus and lessons.
01 The Enterprise Data Platform
Design a modern, scalable enterprise data platform and its architecture.
- The platform mandate: data as a reliable internal product
- Lake, warehouse, and lakehouse architecture at enterprise scale
- Curated data layers and reusable data domains
- Canonical data models across the enterprise
- Aligning the platform to scalability, security, and maintainability
02 Architecture Patterns and Standards
Set the reusable patterns that support analytics, AI, and reporting.
- Reference patterns for reporting, analytics, and AI/ML
- Reusable data domains and product-oriented design
- Enterprise standards for scalability and performance
- Long-term maintainability and technical debt
- Defining the platform's architecture standards
03 Data Engineering and Integration
Build reliable pipelines and integration across enterprise systems.
- Reliable pipelines: batch, streaming, and orchestration
- Integration patterns: ETL, ELT, APIs, and events
- Ensuring data flows are accurate, timely, and resilient
- Exposing engineering and operational systems as services
- Managing the data and integration backlog by value and risk
04 Contract-First Integration and APIs
Design integration as versioned, documented, observable contracts.
- Contract-first design for APIs, events, and batch interfaces
- Schema and version management, and safe evolution
- Testing, error handling, and service-level expectations
- Semantic data products as integration contracts
- Designing a contract-first interface end to end
05 Governance, Quality and MDM
Establish the governance framework the platform runs on.
- The enterprise data governance framework
- Standards for quality, metadata, lineage, and classification
- Master data management and canonical entities
- Data ownership, stewardship, and accountability
- Running governance forums that drive adoption
06 AI-Ready Data Products and Access
Prepare the ecosystem for GenAI, RAG, and agentic systems, responsibly.
- AI-ready data products: ownership, sourcing, and semantics
- Metadata, lineage, quality thresholds, and permitted-use rules
- Data and access patterns for RAG, GenAI, and agents
- Secure access: least privilege, sandbox and prod, audit logging
- Responsible data access and ethical-use governance
07 Observability, Security and Operations
Run the platform reliably, securely, and cost-transparently.
- Observability: freshness, completeness, lineage, latency, cost
- Reliability, performance, monitoring, and incident response
- Access control, encryption, privacy, and compliance
- Cost visibility and platform health reporting
- Partnering with security and compliance on data incidents
08 Capstone: An Enterprise Platform and Integration BlueprintCapstone
Produce a multi-year platform, integration, governance, and AI-ready blueprint.
- Translate enterprise priorities into a platform roadmap
- Design the platform, canonical models, and integration patterns
- Define the governance, MDM, and observability framework
- Specify AI-ready data products and secure access
- Capstone: present an enterprise platform and integration blueprint as a portfolio artifact
Who it's for
Built for Sr. Directors leading enterprise data platform, integration, and AI-ready enablement
Sr. Directors of data platform
Leaders reporting into the CIO who own the enterprise data ecosystem.
Heads of data engineering and integration
Leaders building data engineering, integration, and platform teams.
Enterprise and platform architects
Architects establishing platform standards and patterns.
Senior engineers moving into platform leadership
20+ year practitioners stepping up to own the platform.
Program formats
How you learn
Self-paced
Work through it at your own pace, with lifetime access to every module and the capstone.
Mentor-led cohort
A guided cohort with live sessions, reviews, and a peer group working the same path.
Private corporate
A closed cohort for your team, tailored to your platforms, domains, and priorities.
Portfolio-building
Every module produces an artifact; the capstone assembles them into a portfolio deliverable.
For teams
Bring it to your team
Run Enterprise Data Platform & Integration as a private, closed cohort tailored to your platforms, domains, and priorities, as part of building the architecture capability your organization needs.
Domain & leadership
From the corporate programs
FAQ
Enterprise Data Platform & Integration - answered
Which role does this map to?
The Sr. Director of Enterprise Data Platform, Integration and Architecture, reporting to the CIO: leading the strategy, buildout, and operation of the enterprise data ecosystem and its integration and platform services, to support trusted analytics, automation, and approved AI use cases.
Does it cover integration in depth?
Yes. Contract-first integration, APIs, event streams, batch interfaces, schema and version management, testing, error handling, and service-level expectations are a core focus, alongside ETL, ELT, and streaming.
How does it handle AI-readiness?
A full module covers AI-ready data products, RAG, GenAI, and agentic-workflow enablement, with governance for sensitive data, model inputs, secure access, and ethical use.
Is governance included?
Yes: the enterprise governance framework, quality, metadata, lineage, classification, and MDM, plus running the governance forums that drive adoption.
What is the capstone?
A multi-year enterprise platform, integration, governance, and AI-ready blueprint for a scenario, assembled as a portfolio artifact you can take to a CIO.
Who is it for?
Sr. Directors of data platform, heads of data engineering and integration, enterprise and platform architects, and senior engineers stepping into platform leadership.
Take the next step on the path
Enrol, enquire, or explore the full IC-to-Head of Data Architecture path.