In a large-scale enterprise environment, computing costs can spiral out of control if proper data models and materialization techniques are not implemented. During my previous role, we had queries parsing millions of rows for daily dashboards resulting in significant latency and high credit consumption on Snowflake.

The Solution Strategy:

1. Query Profiling: Identified the top 10% most expensive queries which accounted for 80% of compute cost.
2. Materialized Views: Converted frequently accessed aggregated views (which didn't require immediate real-time sync) into pre-calculated materialized views.
3. Cluster Tuning: Tuned the auto-suspend and auto-resume behaviors on multi-cluster warehouses during off-peak hours.
4. Refactoring Legacy Logic: Migrated slow-running Oracle-era cursors into set-based operations optimized for columnar storage.

Result: Query latency dropped by an average of 45 seconds, and operational compute dropped by 30% within the first month.

Row-Level Security (RLS) is pivotal when delivering a central dashboard across a Fortune 500 company where regional managers should only view their specific territories. Static RLS is simple, but fails to map against a rapidly changing organizational hierarchy of over 1,200 users.

Dynamic RLS Implementation

Instead of hardcoding roles, I established a dynamic RLS mapping table connected to our Azure Active Directory (Entra ID). By utilizing the USERPRINCIPALNAME() DAX function, we seamlessly map the logged-in user to their organizational privileges stored mapped in Snowflake.

• Created a bridge/security table connecting UserPrincipalName to RegionID.
• Applied bi-directional cross-filtering purely across the security bridge to avoid expanding ambiguity in the central fact tables.

This approach eliminated the need to maintain 50+ different dashboard views, merging them into a single, compliant, and scalable model.

As AI tools like GitHub Copilot and ChatGPT become standard practice, translating business requirements into complex DAX logic or Snowflake SQL has radically shifted. But a tool is only as good as the prompt.

Techniques for Better Generations:

• Schema Preamble: Always provide the LLM with a DDL abstract or table schema definitions (tables, relationships, column metadata) before asking it to write a query. Otherwise, it guesses column names.
• Granularity Specification: When generating DAX aggregations, explicitly state the filter context expectations (e.g., 'Ensure this respects the Date dimension slicer but ignores product category').
• Self-Correction Loops: If Copilot creates a circular dependency, asking it to 'review the model for cyclical references based on table X and Y' usually guides it to the correct ALLEXCEPT phrasing.

Through dedicated Prompt Engineering, our BI team's development velocity for ad-hoc requests increased by roughly 25%.