Data Warehouse Design

Builds basic dbt models on top of existing warehouse schemas. Creates simple staging and mart layers following established dimensional modeling conventions. Understands star schema fundamentals and can implement straightforward fact and dimension tables for analytics-ready data marts.

Navigates existing star and snowflake schemas to build reports and dashboards. Understands the difference between fact and dimension tables and writes queries that correctly join them. Uses pre-built aggregate tables for dashboard performance and follows established naming conventions in the BI layer.

Queries warehouse tables using correct join patterns between facts and dimensions. Understands the purpose of analytical schemas and navigates star schema structures to extract meaningful datasets. Writes efficient SELECT statements leveraging partitioning and pre-aggregated tables for routine analysis tasks.

Designs dimensional models and semantic layers that serve multiple downstream consumers. Builds reusable dbt packages with proper materialization strategies, incremental models, and well-documented data marts. Implements slowly changing dimensions and manages schema evolution without breaking existing analytics pipelines.

Designs star and snowflake schemas optimized for BI reporting workloads. Creates aggregate tables and materialized views that significantly improve dashboard query performance. Proposes schema changes to the warehouse team based on reporting requirements and collaborates on dimensional modeling decisions for new data domains.

Designs analytical schemas for specific business domains, choosing appropriate fact and dimension structures. Proposes new warehouse tables and views that improve query efficiency for recurring analysis patterns. Understands trade-offs between normalized and denormalized designs and selects the right approach based on analytical workload characteristics.

Architects data systems with Data Warehouse Design. Optimizes for big data. Implements data governance and quality frameworks.

Architects the BI semantic layer across the entire warehouse, defining conformed dimensions and standardized metrics. Drives schema design decisions that balance reporting flexibility with query performance at scale. Mentors junior analysts on proper schema usage and establishes governance practices for aggregate table lifecycle management.

Leads analytical schema design across business domains, establishing patterns for fact and dimension table construction. Optimizes complex warehouse queries by redesigning underlying schemas and advising on partitioning strategies. Serves as the bridge between data engineering and business teams, translating analytical needs into warehouse architecture requirements.

Defines the analytics warehouse architecture: Kimball vs Data Vault vs One Big Table for different domains, clustering and partitioning strategy. Implements Snowflake/BigQuery best practice standards for dbt projects.

Defines the organization-wide warehouse schema strategy for BI consumption, aligning star and snowflake designs with long-term reporting roadmaps. Establishes standards for aggregate table creation, materialized view governance, and schema versioning. Coordinates with data engineering leadership to ensure warehouse evolution supports both operational and strategic BI initiatives.

Drives the analytical layer strategy across the warehouse, defining how teams model facts and dimensions for cross-domain analysis. Sets standards for schema documentation, query pattern optimization, and analytical table lifecycle. Collaborates with data platform teams to shape warehouse architecture decisions that maximize analytical team productivity and data accessibility.

Architects the enterprise data warehouse strategy: multi-warehouse for different workloads (analytics, ML, reporting), cost governance through resource monitors. Defines data sharing architecture between business units and external partners.

Shapes the enterprise data warehouse vision for BI, driving adoption of modern patterns like data mesh semantic layers and unified metrics platforms. Evaluates emerging warehouse technologies and their impact on BI schema design at organizational scale. Influences vendor and platform decisions, ensuring warehouse architecture enables self-service analytics while maintaining data quality and governance standards.

Defines the enterprise analytical data architecture, establishing how warehouse schemas evolve to support advanced analytics, ML feature stores, and cross-functional data products. Drives strategic decisions on warehouse platform selection and schema paradigms across the organization. Champions data democratization by designing warehouse structures that empower analysts at all levels to access and interpret data independently.