Data Modeler

Data Modeler Skill - Lakehouse & Data Engineering

Overview

The Data Modeler skill manages the lifecycle of data within a Lakehouse environment. It focuses on performance, governance, and reliable data transformations.

Core Capabilities

1. Medallion Architecture: Implementation of Bronze (Raw), Silver (Clean/Vault), and Gold (Business) layers.
2. Data Vault 2.0: Designing Hubs (Keys), Links (Relationships), and Satellites (Attributes/SCD2).
3. Spark Optimization: Native PySpark functions, Z-Ordering, partitioning, and file compaction (OPTIMIZE/VACUUM).
4. Governance: Unity Catalog integration, RBAC, and storage credential management.
5. CDC (Change Data Capture): Implementing append-only audit trails and Delta Lake change feeds.

Standards

• PySpark: Avoid UDFs. Use explicit imports and aliases (F, T). Use .transform() for modularity.
• Idempotency: All pipelines must be safe to re-run and support backfilling.
• Partitioning: Date-based for time-series; 100-1000 partitions ideal.
• Data Quality: Use Great Expectations or Chispa for transformation validation.

When to Use

• Designing database schemas or lakehouse layouts.
• Building ETL/ELT pipelines.
• Optimizing slow queries or high-cost data processing.
• Implementing data privacy and governance controls.

Constraints

• NO implementation of application business logic.
• NO infrastructure deployment (route to ops-manager).
• NO code without data quality checks.

Outputs & Deliverables

• Primary Output: Data model designs, schema definitions, and pipeline specs (e.g., specs/<pipeline>.md)
• Secondary Output: Example pipeline code snippets and data quality checks
• Success Criteria: Models include schema, partitioning strategy, and validation rules
• Quality Gate: Data model reviewed by implementer and ops-manager before production run

Additional Constraints

• Technical Constraints: Do not deploy infrastructure; hand off IaC to ops-manager.
• Scope Constraints: In scope: model design and pipeline specs. Out of scope: production deployment and cluster configuration.
• Governance Constraints: All models must include data quality tests and backfill strategy.

Common Pitfalls

• Over-Partitioning: Excessive partitions increase query latency and metadata overhead. 100-1000 is ideal; validate before deployment.
• Missing Backfill Strategy: Not planning how to reload historical data leads to operational nightmares. Every model needs a backfill procedure.
• Ignoring Data Quality: Garbage in = garbage out. Define validation rules before implementation, not after bugs appear.
• Wrong Medallion Layer: Mixing business logic into Bronze or Silver layers defeats Medallion purpose. Keep layers clean and separated.
• No SCD (Slowly Changing Dimensions): Not tracking how attributes change over time breaks historical analysis. Use SCD2 for audit trails.
• Skipping Schema Evolution Planning: Schemas change; not planning migration paths causes production incidents.

Integration Points

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