Experienced Data Engineering professional specializing in designing and implementing enterprise-grade data models (conceptual, logical, and physical) aligned with business objectives and scalable cloud architectures. Proven expertise in building high-performance Data Warehouse solutions using Snowflake, with a strong focus on data modeling standards, performance optimization, and scalability.

Skilled in dimensional modeling (Kimball) and Data Vault 2.0, with hands-on experience in developing optimized fact and dimension tables to support large-scale analytics and BI workloads. Adept at creating end-to-end data warehouse architectures (Staging → Core → Presentation layers) ensuring efficiency, governance, and data quality.

Strong proficiency in advanced SQL for complex transformations, validation, and query optimization, combined with experience in clustering, partitioning, and Snowflake performance tuning. Collaborates closely with ETL teams to design and implement robust, scalable data pipelines, ensuring seamless data integration and processing.

Demonstrated ability to conduct impact analysis, enforce data governance and metadata management, and translate complex business requirements into scalable, future-ready data solutions that drive business insights and decision-making.

Design and implement enterprise-grade data models (conceptual, logical, and physical) aligned with business requirements and industry standardsDefine and enforce data modeling standards, best practices, and governance frameworks across the data platformArchitect and support Snowflake-based data warehouse solutions, ensuring optimal performance, scalability, and cost efficiencyDevelop and maintain high-performance Data Warehouse schemas using Star and Snowflake models for analytics and reportingApply Dimensional Modeling (Kimball) and Data Vault 2.0 methodologies to build scalable and flexible data architecturesEngineer and optimize Fact and Dimension tables to support enterprise-wide BI and large-scale reporting workloadsPerform impact analysis to assess downstream effects of model changes and ensure alignment with evolving business needsLeverage Snowflake capabilities such as clustering, partitioning, and query optimization to enhance performanceWrite and optimize advanced SQL queries for complex data transformations, validations, and performance tuningCollaborate with ETL/ELT teams to design and implement efficient, scalable data pipelines for seamless data ingestion and processingBuild and manage end-to-end data warehouse architecture (Staging → Core → Presentation/BI layers)Ensure data quality, integrity, governance, and metadata management across the data ecosystemTranslate complex business requirements into scalable, future-ready data models and solutionsWork closely with stakeholders, architects, and analytics teams to enable data-driven decision-making

Key Skill Requirements (Snowflake Data Modeling / Data Engineering Role)

🔹 Core Data Modeling Skills

• Expertise in Conceptual, Logical, and Physical Data Modeling
• Strong experience in ER Modeling (Entity-Relationship Design)
• Proficiency in Dimensional Modeling (Kimball methodology)
• Hands-on knowledge of Data Vault 2.0 architecture
• Ability to design Fact and Dimension tables for large-scale analytics

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🔹 Snowflake & Cloud Data Platform

• Strong working experience with Snowflake Data Warehouse
• Knowledge of Snowflake architecture, clustering, and performance tuning
• Ability to design scalable and optimized data structures in cloud environments

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🔹 Data Warehousing Expertise

• Experience in building enterprise Data Warehouse solutions
• Proficiency in Star Schema and Snowflake Schema design
• Knowledge of end-to-end DW architecture (Staging → Core → Presentation/BI layers)

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🔹 SQL & Data Processing

• Advanced proficiency in SQL (complex queries, joins, transformations)
• Experience in query optimization and performance tuning
• Ability to handle large-scale data transformations and validations

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🔹 ETL / Data Engineering

• Strong understanding of ETL/ELT concepts and data pipelines
• Experience collaborating with ETL tools/teams (e.g., Informatica, ADF, etc.)
• Ability to design efficient, scalable data ingestion and transformation pipelines

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🔹 Data Governance & Quality

• Knowledge of data governance frameworks and standards
• Experience ensuring data quality, integrity, and consistency
• Familiarity with metadata management and data lineage practices

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🔹 Analytical & Business Alignment

• Ability to translate business requirements into data models
• Experience conducting impact analysis for data model changes
• Strong understanding of BI/reporting requirements and analytics use cases

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🔹 Performance & Scalability

• Skills in data performance optimization and scalability design
• Experience in handling high-volume datasets and enterprise workloads

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