Description

This curriculum spans the technical and organizational challenges encountered in multi-year enterprise application development programs, comparable to the scoping and governance efforts seen in large-scale system integrations and internal platform builds.

Module 1: Defining Application Scope and Stakeholder Alignment

Selecting which business units will have decision rights over feature prioritization in cross-functional enterprise applications
Documenting conflicting requirements from legal, compliance, and operations teams and establishing a change control board to resolve them
Determining whether to build a monolithic system or decompose functionality across bounded contexts based on organizational maturity
Negotiating data ownership between departments when designing shared customer master records
Establishing escalation paths for requirement disputes when business sponsors cannot reach consensus on MVP scope
Choosing between adopting corporate design systems or allowing product teams to maintain independent UI frameworks

Module 2: Architecture Design and Technology Stack Selection

Evaluating whether to use an existing enterprise service bus or adopt event-driven microservices with Kafka
Deciding on database sharding strategies when projecting user growth beyond 10 million records per table
Assessing vendor lock-in risks when selecting cloud-native services like AWS Lambda vs. containerized open-source alternatives
Implementing circuit breakers and retry policies in inter-service communication to prevent cascading failures
Choosing between gRPC and REST for internal APIs based on performance requirements and team expertise
Enforcing API versioning policies and deprecation timelines across multiple consuming teams

Module 3: Security, Compliance, and Identity Management

Integrating with existing SAML-based identity providers versus implementing OIDC in hybrid cloud environments
Designing role-based access control (RBAC) hierarchies that reflect organizational reporting structures and segregation of duties
Implementing audit logging standards that satisfy SOX requirements for financial transaction systems
Configuring secrets management using HashiCorp Vault versus cloud provider-native solutions like AWS Secrets Manager
Conducting threat modeling sessions to identify attack surfaces in new application workflows
Enforcing encryption of data at rest and in transit when data residency laws require storage within specific geographic regions

Module 4: Data Management and Integration Strategy

Choosing between real-time CDC (Change Data Capture) and batch ETL for synchronizing data across legacy and modern systems
Designing idempotent message processors to handle duplicate events from unreliable upstream systems
Resolving schema drift issues when consuming data from third-party APIs with inconsistent versioning
Implementing data retention and archival policies that comply with GDPR right-to-be-forgotten requests
Building data quality checks into ingestion pipelines to prevent propagation of malformed records
Selecting between GraphQL and traditional REST endpoints for aggregating data from multiple backend services

Module 5: Development Workflow and CI/CD Implementation

Enforcing pull request review requirements across distributed teams operating in different time zones
Configuring canary deployments with traffic shifting based on error rate and latency thresholds
Managing dependency updates across shared libraries without breaking backward compatibility
Designing test environments that mirror production data volumes and configurations within budget constraints
Implementing feature flags with kill switches for high-risk functionality releases
Standardizing logging formats and correlation IDs across services to enable distributed tracing

Module 6: Observability, Monitoring, and Incident Response

Defining SLOs and error budgets for customer-facing applications and communicating them to product teams
Configuring alerting rules to minimize false positives while ensuring critical system failures are detected
Setting up synthetic monitoring for key user journeys to detect degradation before real users are impacted
Establishing on-call rotations and escalation procedures for production incidents across global engineering teams
Integrating application performance metrics with existing enterprise monitoring dashboards
Conducting blameless postmortems and tracking remediation tasks to closure after major outages

Module 7: Scalability, Performance, and Cost Optimization

Right-sizing Kubernetes clusters based on workload patterns and autoscaling behavior
Implementing caching strategies at multiple layers (CDN, application, database) to reduce backend load
Optimizing database query performance by analyzing slow query logs and adding targeted indexes
Negotiating reserved instance purchases versus using spot instances for non-critical batch processing
Conducting load testing to validate system behavior under peak traffic conditions before major releases
Identifying and eliminating redundant API calls in frontend applications to improve responsiveness

Module 8: Application Lifecycle and Technical Debt Governance

Establishing criteria for when to refactor, retire, or replace legacy applications based on maintenance costs
Creating a technical debt register and prioritizing remediation work alongside feature development
Enforcing code quality gates in CI pipelines using static analysis and test coverage thresholds
Managing dependencies on deprecated frameworks when vendor support ends
Documenting system context and data flows for onboarding new engineers to complex applications
Planning for sunsetting integrations when upstream systems are decommissioned