Data engineer SQL + Data Modeling

SQL Developers / Analytics Engineers:SQL Expertise

• Mastery of window functions: Proficient with ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE(), LEAD(), LAG(), and other analytic functions for complex data analysis.
• Advanced aggregation techniques: Skilled in using GROUPING SETS, ROLLUP, and HAVING for multidimensional summaries.
• Recursive queries: Able to write and optimize recursive Common Table Expressions (CTEs) for hierarchical or complex data structures.
• Dynamic SQL: Experienced in constructing and executing SQL statements dynamically for flexible query logic.
• Pivoting and unpivoting data: Uses PIVOT and UNPIVOT operations to reshape datasets for reporting and analysis.
• Error handling and control-of-flow: Uses TRY...CATCH, CASE, and conditional logic to manage complex workflows and exceptions.
• Performance optimization: Understands how advanced functions impact query plans and applies indexing, partitioning, and statistics to maintain performance.

Data Modeling & Schema Design

• Deep understanding of dimensional modeling: Designs star and snowflake schemas optimized for analytical workloads.
• Expert in fact and dimension table design: Defines grain, identifies facts, and creates conformed and slowly changing dimensions (SCD Types 1-2).
• Skilled in ETL architecture: Designs robust ETL pipelines aligned with Kimballs bus architecture and data staging strategies.
• Business process-centric modeling: Models data around core business processes to ensure alignment with organizational goals.
• Mastery of surrogate keys and late-arriving dimensions: Applies best practices for data integrity and historical tracking.
• Understands Kimballs data warehouse lifecycle: Applies the full methodology from requirements gathering to deployment and maintenance.
• Performance-aware design: Optimizes models for query performance, including indexing, partitioning, and aggregation strategies.
• Cross-functional collaboration: Works closely with analysts, engineers, and BI developers to ensure models meet business needs.
• Documentation and metadata management: Maintains clear documentation and supports metadata-driven development.