Description

This curriculum spans the technical and operational rigor of a multi-workshop data engineering program, covering the breadth of responsibilities handled in enterprise data platform teams, from secure ingestion and schema governance to privacy-preserving transformation and production pipeline observability.

Module 1: Data Ingestion Architecture in OKAPI

Select between batch and streaming ingestion based on source system SLAs and downstream latency requirements
Configure secure credential rotation for accessing enterprise data sources via OAuth, Kerberos, or managed service identities
Implement schema versioning at ingestion to handle backward-incompatible changes from upstream systems
Design fault-tolerant data pipelines with retry logic and dead-letter queues for malformed records
Integrate metadata harvesting at the point of ingestion to populate data lineage registries
Apply data retention policies during ingestion to comply with GDPR and internal data governance rules
Validate payload size and frequency thresholds to prevent pipeline overloads from high-volume sources

Module 2: Schema Harmonization and Standardization

Define canonical field names and data types across disparate source systems using a centralized data dictionary
Resolve conflicting business definitions (e.g., "revenue" vs. "net sales") through cross-functional stakeholder alignment
Implement schema evolution strategies to support backward and forward compatibility in data contracts
Map legacy codes (e.g., product categories) to standardized taxonomies using reference data services
Automate schema drift detection using statistical profiling and alerting on structural anomalies
Enforce schema conformance using declarative validation rules before data progresses to transformation
Handle optional vs. required fields based on business criticality and downstream model dependencies

Module 3: Data Quality Assessment and Monitoring

Establish data quality KPIs (completeness, accuracy, consistency) per data domain and stakeholder SLAs
Deploy automated anomaly detection on statistical profiles (e.g., null rates, value distributions)
Configure alerting thresholds for data quality violations with escalation paths to data stewards
Implement reconciliation checks between source and target row counts and aggregate totals
Log data quality rule outcomes for auditability and root cause analysis in production incidents
Balance false positive alerts against detection sensitivity to maintain operational trust
Integrate data quality dashboards into existing observability platforms (e.g., Datadog, Splunk)

Module 4: Entity Resolution and Record Linkage

Select deterministic vs. probabilistic matching strategies based on data availability and precision requirements
Design blocking rules to reduce pairwise comparison complexity in large-scale customer datasets
Calibrate match thresholds to balance false merges and missed links in golden record creation
Manage identity resolution across organizational boundaries with privacy-preserving techniques
Implement survivorship rules to resolve conflicting attribute values from multiple source systems
Maintain audit trails of merge/split operations for compliance and rollback capability
Integrate with MDM systems to synchronize canonical entity identifiers across platforms

Module 5: Temporal Data Handling and Point-in-Time Correctness

Model slowly changing dimensions using hybrid Type 2/Type 6 approaches for analytical accuracy
Synchronize event time vs. ingestion time across pipelines to ensure temporal consistency
Implement point-in-time joins to reconstruct historical states for time-travel analytics
Manage timezone normalization and daylight saving transitions in timestamp fields
Handle late-arriving data with watermarking and reprocessing strategies in streaming contexts
Preserve effective date ranges in master data to support audit and regulatory reporting
Optimize temporal queries using clustering and partitioning on time keys in data warehouses

Module 6: Privacy-Preserving Data Transformation

Apply tokenization or format-preserving encryption to sensitive fields in non-production environments
Implement role-based data masking at the transformation layer based on user entitlements
Conduct data minimization by removing unnecessary PII before downstream propagation
Integrate with enterprise data classification tools to dynamically apply protection rules
Validate anonymization efficacy using re-identification risk scoring models
Log access and transformation of sensitive data for privacy impact assessments
Coordinate with legal teams to align data masking policies with jurisdictional regulations

Module 7: Scalable Feature Engineering Pipelines

Design reusable feature templates to standardize calculation logic across use cases
Optimize window function usage in SQL-based feature derivation to avoid performance bottlenecks
Cache and version engineered features to support reproducible model training and serving
Implement feature drift detection by monitoring statistical properties over time
Synchronize feature computation between batch and real-time pipelines using dual-write patterns
Register features in a central feature store with metadata on ownership, latency, and usage
Enforce data type consistency and missing value handling in feature generation logic

Module 8: Metadata Management and Data Lineage

Automatically extract technical lineage from ETL job execution logs and SQL parsers
Link business glossary terms to physical data assets using semantic tagging
Implement impact analysis capabilities to assess downstream effects of source changes
Synchronize metadata across tools (e.g., data catalog, BI platforms, ML systems) via APIs
Track data ownership and stewardship assignments within the metadata repository
Archive historical metadata versions to support audit and regulatory inquiries
Enforce metadata completeness as a gate in CI/CD pipelines for data transformations

Module 9: Operationalization and Pipeline Governance

Define SLA tiers for pipeline execution frequency, latency, and uptime by data criticality
Implement CI/CD for data pipelines with automated testing and deployment rollback capability
Configure centralized logging and monitoring with structured log schemas for root cause analysis
Enforce data pipeline access controls using role-based permissions and separation of duties
Conduct production readiness reviews covering scalability, resilience, and supportability
Manage configuration drift using version-controlled infrastructure-as-code templates
Schedule and document periodic pipeline health assessments and technical debt remediation