Data Architect

Job description

Strong expertise in Kimball Dimensional Modeling, Dimensional Data Modeling, Star & Snowflake Schemas, Fact & Dimension Tables, SCD, Data Warehouse Design, Data Vault Modeling, and Enterprise Data Modeling. Experience creating Conceptual, Logical, and Physical Data Models using industry-standard methodologies. Hands-on experience with ER/Studio, Erwin Data Modeler, PowerDesigner, or similar data modeling tools. Strong knowledge of Azure Data Factory (ADF), Azure Databricks, Azure Data Lake Storage (ADLS), and Data Lakehouse Architectures. Experience designing and optimizing ETL/ELT pipelines, data integration solutions, and data orchestration workflows. Proficiency in SQL and Python for data transformation, automation, and analytics. Experience integrating data using REST APIs and implementing scalable data ingestion frameworks. Design and implement scalable enterprise data architectures to support business intelligence, analytics, and operational reporting. Lead the development of Kimball dimensional models, including star schemas, fact tables, dimension tables, and Slowly Changing Dimensions (SCDs). Create and maintain conceptual, logical, and physical data models using industry-standard data modeling tools. Collaborate with Data Analysts, Data Engineers, Data Governance teams, and business stakeholders to translate business requirements into robust data solutions. Architect and optimize data pipelines for efficient data ingestion, integration, transformation, and storage. Ensure data models align with enterprise data governance, quality, security, and compliance standards. Support real-time and historical analytics by designing scalable and high-performance data warehouse solutions. Drive best practices in data architecture, metadata management, master data management (MDM), and data lifecycle management. Integrate data from multiple enterprise and operational systems, including manufacturing, engineering, ERP, and external platforms. Enable advanced analytics, AI/ML initiatives, and GenAI use cases through well-structured and governed data foundations.