Senior Data Warehouse Architect

3 days ago

bangalore district, India ThoughtVerve Full time

Company Description ThoughtVerve is a comprehensive solutions provider offering tailored software and technology services in design, development, and deployment to clients worldwide. We emphasize future-proof solutions characterized by scalability, robustness, maintainability, and adaptability to technological advancements. Our commitment extends from the initial concept phase through client evolution, ensuring continual support and decisive outcomes. Role Description Immediate joiners needed We are seeking a highly experienced Senior Data Architect with deep expertise in Business Intelligence (BI) and cloud data platforms. The ideal candidate will be responsible for designing, implementing, and managing enterprise data architecture, ensuring data integrity, scalability, and analytics readiness. You will collaborate with business and technology teams to translate organizational data needs into robust, modern data solutions. Key Responsibilities • Data Strategy and Architecture: Defining the overall data architecture, frameworks, and standards for the organization, including data lakes, data warehouses, and data marts, to support BI and analytics goals. • Data Modeling: Designing and maintaining conceptual, logical, and physical data models that are optimized for reporting and analysis. • BI Platform Leadership: Selecting, integrating, and optimizing the Business Intelligence (BI) tools and platforms (e.g., Tableau, Power BI, Cognos) to ensure they can connect to, query, and visualize data effectively. • ETL/ELT Design: Defining the Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) strategy and architecture for moving data from source systems into the analytical data store / data warehouse on AWS environment. • Performance Optimization: Ensuring the data architecture and BI solutions are scalable, secure, and performant, particularly for complex and high-volume queries run by BI tools and analysts. • Data Governance and Quality: Establishing policies for data quality, data lineage, metadata management, and security (access controls) to ensure the integrity and trustworthiness of the data used for BI. • Drive adoption of data best practices, documentation, and metadata management.

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