Snowflake Data Architect

The Data Architect works in all data environments which includes data design, database architecture, metadata and repository creation. The Data Architect work assignments are varied and frequently require interpretation and Consultant determination of the appropriate courses of action. The Data Architect responsible for developing blueprints for all data repositories, evaluating hardware and software platforms, and integrating systems. Translates business needs into long-term data architecture solutions. Defines, designs and builds dimensional database schemas. Evaluates reusability of current data for separate analyses. Conducts data sheering to rid the system of old, unused or duplicate data. Reviews object and data models and the metadata repository to structure the data for better management and quicker access. Understands department, segment, and organizational strategy and operating objectives, including their linkages to related areas. Makes decisions regarding own work methods, occasionally in ambiguous situations, and requires minimal direction and receives guidance where needed. Follows established guidelines/procedures. Data Architecture & Strategy Define and maintain enterprise data architecture standards, principles, and roadmaps. Design scalable data platforms supporting analytics, reporting, AI/ML, and operational workloads. Develop conceptual, logical, and physical data models for enterprise applications. Establish architecture patterns for data integration, master data management, and data quality. Cloud Data Platform Leadership Lead the design and implementation of cloud-based data platforms using technologies such as Snowflake, Prefect, Google Cloud Platform (Eligible to workP), Azure and Databricks Define best practices for data storage, processing, orchestration, and security. Guide migration of on-premises data assets to cloud environments. Data Engineering & Integration Architect batch, real-time, and event-driven data pipelines. Design solutions for API integrations, ETL/ELT frameworks, and data virtualization. Ensure high-performance and scalable ingestion, transformation, and consumption of data. Governance & Compliance Partner with Data Governance teams to establish metadata management, lineage, stewardship, and data quality frameworks.

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