Description

This curriculum spans the technical and operational breadth of a multi-workshop data platform rollout, covering the design, governance, and optimization of data systems as practiced in large-scale internal capability programs.

Module 1: Defining Data Requirements for Business Alignment

Collaborate with business stakeholders to translate KPIs into measurable data entities and attributes, ensuring traceability from metric to source system.
Document lineage of critical business metrics by mapping them to source systems, transformation logic, and reporting layers.
Negotiate data freshness requirements (e.g., real-time vs. daily batch) based on operational impact and technical feasibility.
Identify and classify data domains (e.g., customer, product, transaction) to establish ownership and stewardship boundaries.
Specify data quality thresholds (completeness, accuracy, timeliness) per use case and integrate them into ingestion validation rules.
Resolve conflicting data definitions across departments by facilitating cross-functional data governance workshops.
Design metadata capture processes to record business context, definitions, and usage patterns alongside technical metadata.
Establish versioning policies for business metrics to manage changes without breaking historical reporting.

Module 2: Data Sourcing and Ingestion Architecture

Select ingestion patterns (batch, micro-batch, CDC, streaming) based on source system capabilities and downstream latency needs.
Implement change data capture using log-based tools (e.g., Debezium) for transactional databases while managing performance impact.
Configure retry and backpressure mechanisms in streaming pipelines to handle downstream system outages.
Design schema evolution strategies for incoming data using schema registries and backward-compatible formats (e.g., Avro).
Encrypt sensitive data in transit and at rest during ingestion, applying key management policies aligned with compliance standards.
Handle API rate limits and pagination when extracting from third-party SaaS platforms using exponential backoff and queuing.
Implement idempotent ingestion logic to prevent data duplication during retries or pipeline restarts.
Monitor ingestion pipeline lag and throughput to detect performance degradation or source system issues.

Module 3: Data Modeling for Analytical Workloads

Choose between normalized, dimensional, and data vault modeling based on query patterns, auditability, and change frequency.
Design slowly changing dimensions (SCD Type 2) with effective dating and hash keys to track historical attribute changes.
Partition large fact tables by time and filter frequently used dimensions to optimize query performance.
Denormalize specific dimensions selectively to reduce join complexity in high-latency reporting environments.
Implement surrogate key generation strategies that support scalability and integration across multiple source systems.
Balance granularity of fact tables against storage costs and query performance for common analytical use cases.
Model semi-structured data (e.g., JSON logs) using flattening, nested types, or external tables based on access patterns.
Define conformed dimensions to enable consistent reporting across business units and data marts.

Module 4: Pipeline Orchestration and Workflow Management

Structure DAGs to reflect data dependencies while minimizing cross-workflow coupling and runtime bottlenecks.
Implement dynamic pipeline generation for repetitive tasks (e.g., per-region loads) using templating and configuration files.
Configure alerting and notification rules for failed tasks, missed SLAs, and unexpected data volume shifts.
Integrate pipeline testing into CI/CD workflows using synthetic data and mock dependencies.
Manage execution priority and resource allocation for critical vs. non-critical pipelines in shared environments.
Version control pipeline definitions and coordinate deployments across development, staging, and production environments.
Handle backfilling historical data without disrupting ongoing scheduled runs or overloading downstream systems.
Monitor task duration trends to detect performance regressions or infrastructure constraints.

Module 5: Data Quality and Observability

Embed data validation checks (e.g., null counts, value ranges, referential integrity) at ingestion and transformation stages.
Define and track freshness SLAs for datasets, triggering alerts when expected updates are delayed.
Implement automated anomaly detection on data distributions using statistical baselines and threshold rules.
Log data quality rule outcomes to a central observability platform for audit and trend analysis.
Design quarantine zones for suspect data to prevent contamination of downstream analytics while enabling investigation.
Correlate data pipeline failures with upstream system outages or schema changes using integrated monitoring tools.
Generate data health dashboards that expose quality metrics to both technical and business stakeholders.
Respond to data incidents with root cause analysis and implement preventive controls to avoid recurrence.

Module 6: Data Storage and Platform Selection

Evaluate cloud data warehouse options (e.g., Snowflake, BigQuery, Redshift) based on concurrency, cost model, and integration needs.
Design data lake zone architecture (raw, curated, trusted) with access controls and lifecycle policies.
Select file formats (Parquet, ORC, Delta Lake) based on compression, schema evolution, and transaction support requirements.
Implement data partitioning and clustering strategies to reduce query scan costs in large datasets.
Configure storage tiering (hot, cold, archive) and lifecycle rules to manage cost for historical data.
Balance compute-storage separation benefits against data locality needs for high-throughput workloads.
Apply object storage encryption and bucket policies to enforce compliance with data residency regulations.
Plan for cross-region replication and disaster recovery for mission-critical datasets.

Module 7: Metadata Management and Data Discovery

Integrate technical metadata (schema, lineage, usage) from ingestion, transformation, and query layers into a central catalog.
Automate metadata extraction using hooks in orchestration tools and query engines (e.g., parsing Airflow DAGs, BigQuery audit logs).
Implement business metadata tagging to link datasets to owners, domains, and governed metrics.
Design search and discovery interfaces that support filtering by data sensitivity, freshness, and usage frequency.
Track data lineage across transformations, including column-level lineage for regulatory compliance.
Enforce metadata completeness as a gate in deployment pipelines to prevent undocumented datasets from reaching production.
Manage metadata retention policies to avoid performance degradation in large-scale catalogs.
Integrate data catalog with access control systems to dynamically filter discoverable datasets based on user permissions.

Module 8: Data Governance and Access Control

Define data classification levels (public, internal, confidential, restricted) and apply labels consistently across platforms.
Implement attribute-based or role-based access controls in data warehouses and lakes to enforce least-privilege principles.
Automate provisioning and deprovisioning of data access through integration with identity providers (e.g., Okta, Azure AD).
Conduct access reviews for sensitive datasets on a regular cadence to remove stale permissions.
Log and audit all data access and query activities for compliance and forensic analysis.
Enforce data masking and row-level security policies based on user roles or department affiliations.
Establish data stewardship roles and workflows for approving access requests and resolving quality issues.
Align data governance policies with regulatory frameworks (e.g., GDPR, CCPA) and conduct gap assessments.

Module 9: Performance Optimization and Cost Management

Right-size compute clusters or virtual warehouses based on historical workload patterns and peak demand.
Implement materialized views or summary tables for frequently accessed aggregations to reduce query load.
Optimize query plans by analyzing execution profiles and rewriting inefficient SQL or DataFrame operations.
Monitor and control runaway queries using time and resource limits enforced at the platform level.
Track cost attribution by team, project, or workload using tagging and usage reporting tools.
Negotiate reserved capacity or flat-rate pricing models for predictable workloads to reduce variable costs.
Automate suspension of development environments during non-business hours to reduce idle spend.
Conduct regular cost reviews to identify underutilized datasets, redundant pipelines, or inefficient storage patterns.

Data Engineering in Data Driven Decision Making