Description

This curriculum spans the breadth of a multi-workshop program on enterprise data platform development, addressing the technical, governance, and collaboration challenges encountered in large-scale data warehouse and analytics initiatives across distributed teams.

Module 1: Defining Strategic Objectives and Aligning Data Initiatives

Selecting KPIs that directly influence executive decision-making versus those that support operational monitoring
Mapping data use cases to business outcomes during stakeholder workshops with product and finance teams
Deciding whether to prioritize quick-win analytics projects or foundational data infrastructure improvements
Establishing criteria for terminating low-impact analytics initiatives despite sunk costs
Negotiating data ownership between business units when objectives conflict
Documenting decision rationales for auditability when aligning data roadmaps with corporate strategy
Choosing between centralized and federated data governance models based on organizational maturity
Integrating regulatory constraints into objective-setting for global data products

Module 2: Data Sourcing, Ingestion, and Pipeline Architecture

Designing idempotent ingestion workflows to handle duplicate or out-of-order data from transactional systems
Selecting batch frequency based on SLA requirements and source system performance thresholds
Implementing schema evolution strategies in streaming pipelines using schema registry tools
Choosing between change data capture and API polling based on source system capabilities
Configuring retry logic and dead-letter queues for failed records in distributed pipelines
Assessing cost-performance trade-offs between cloud-native ingestion services and self-managed clusters
Enforcing data type consistency during ingestion from heterogeneous sources
Implementing data provenance tracking at the record level for compliance and debugging

Module 3: Data Modeling for Analytical Workloads

Choosing star schema over normalized models based on query performance requirements
Defining conformed dimensions to enable cross-business-unit reporting consistency
Handling slowly changing dimensions (Type 2) with effective date ranges and hash keys
Denormalizing tables in data marts when join latency exceeds reporting SLAs
Implementing surrogate keys to decouple analytical models from source system identifiers
Designing partitioning and clustering strategies in cloud data warehouses for cost control
Managing model drift when source system semantics change without notification
Documenting business logic in transformation layers to prevent analytical misinterpretation

Module 4: Data Quality Monitoring and Validation

Setting threshold-based alerts for null rates in critical fields like customer ID or transaction amount
Implementing statistical baselines for numerical distributions to detect silent data corruption
Automating validation rules across staging, warehouse, and consumption layers
Classifying data issues by severity to prioritize remediation efforts
Integrating data quality metrics into CI/CD pipelines for data models
Handling missing data from third-party vendors with fallback sources or imputation policies
Logging validation failures with context for root cause analysis by engineering teams
Defining ownership for data quality SLAs across data engineering and domain teams

Module 5: Feature Engineering and Dataset Curation

Deciding whether to compute rolling aggregates in batch or real-time based on use case
Managing feature freshness requirements for ML models versus reporting dashboards
Versioning datasets to ensure reproducibility of model training and evaluation
Implementing feature stores with consistency guarantees across training and serving
Handling categorical variable expansion for high-cardinality identifiers
Applying data masking or generalization to sensitive features in shared datasets
Documenting feature derivation logic for regulatory review in financial or healthcare domains
Optimizing feature storage format and compression for query performance in large-scale training

Module 6: Decision Systems and Model Integration

Designing fallback mechanisms for real-time scoring APIs during model deployment outages
Implementing A/B testing frameworks to compare model-driven decisions against business rules
Logging model inputs and outputs for post-decision auditing and bias analysis
Choosing between embedded scoring in databases versus external microservices
Managing model version rollback procedures when performance degrades in production
Integrating human-in-the-loop validation for high-risk automated decisions
Enforcing input validation at the inference layer to prevent model drift from data shift
Configuring model monitoring for prediction drift and outlier detection in production

Module 7: Access Control, Privacy, and Regulatory Compliance

Implementing row-level security policies based on user roles and data sensitivity
Applying dynamic data masking for PII in non-production environments
Conducting data protection impact assessments for new analytics projects
Managing data retention schedules in alignment with GDPR and CCPA requirements
Configuring audit logging for data access in cloud data warehouses
Designing anonymization techniques for datasets used in external research collaborations
Enforcing data minimization principles during feature selection for ML models
Responding to data subject access requests using metadata and lineage systems

Module 8: Performance Optimization and Cost Management

Right-sizing compute clusters based on historical query patterns and concurrency needs
Implementing materialized views for frequently accessed aggregations
Setting auto-pause and auto-scaling policies for cloud data warehouse instances
Optimizing query patterns to minimize data scanned in object storage
Establishing cost allocation tags for chargeback across departments
Archiving cold data to lower-cost storage tiers with access trade-offs
Enforcing query timeouts and resource limits to prevent runaway jobs
Conducting regular cost reviews with engineering leads to identify inefficiencies

Module 9: Change Management and Cross-functional Collaboration

Documenting data model changes in changelogs accessible to business analysts
Coordinating downtime windows for data pipeline maintenance with downstream teams
Standardizing naming conventions across data assets to reduce onboarding time
Facilitating data literacy sessions for non-technical stakeholders using real datasets
Resolving conflicting metric definitions between finance and operations teams
Managing communication plans for deprecating legacy data sources
Establishing escalation paths for data incident response across time zones
Integrating data documentation into existing project management workflows