Virtusa
Description
Requirements Design and implement data models: Develop conceptual, logical, and physical data models that meet business requirements. Develop data warehousing strategies: Design and implement data warehouses and data lakes to support business intelligence and analytics. Establish data governance policies: Define and enforce data quality standards, access controls, and security protocols. Collaborate with stakeholders: Work with data scientists, engineers, and business analysts to understand data needs and translate them into technical solutions. Evaluate and select data management tools: Research and recommend appropriate data management technologies and tools. Oversee data migration and integration: Manage the process of migrating data from legacy systems to new platforms. Performance tuning and optimization: Monitor and optimize data infrastructure performance to ensure efficient data access. Stay up-to-date with industry trends: Keep abreast of the latest developments in data management technologies and best practices. Skills Data modeling: Expertise in various data modeling techniques, including relational, dimensional, and NoSQL. Data warehousing: Strong understanding of data warehousing concepts and experience with data warehouse design and implementation. ETL processes: Proficiency in designing and implementing Extract, Transform, Load (ETL) processes. Database management systems (DBMS): Extensive knowledge of various DBMS platforms, such as SQL Server, Oracle, and MySQL. Big data technologies: Familiarity with big data technologies like Hadoop, Spark, and NoSQL databases. Business Intelligence: Experience with various BI tools and technologies including Tableau, PowerBI, Microstrategy, etc., Data governance: Understanding of data governance principles and best practices. Cloud computing: Experience with cloud-based data platforms like AWS, Azure, and GCP. Communication and collaboration: Excellent communication and interpersonal skills to effectively collaborate with stakeholders
Requirements Design and implement data models: Develop conceptual, logical, and physical data models that meet business requirements. Develop data warehousing strategies: Design and implement data warehouses and data lakes to support business intelligence and analytics. Establish data governance policies: Define and enforce data quality standards, access controls, and security protocols. Collaborate with stakeholders: Work with data scientists, engineers, and business analysts to understand data needs and translate them into technical solutions. Evaluate and select data management tools: Research and recommend appropriate data management technologies and tools. Oversee data migration and integration: Manage the process of migrating data from legacy systems to new platforms. Performance tuning and optimization: Monitor and optimize data infrastructure performance to ensure efficient data access. Stay up-to-date with industry trends: Keep abreast of the latest developments in data management technologies and best practices. Skills Data modeling: Expertise in various data modeling techniques, including relational, dimensional, and NoSQL. Data warehousing: Strong understanding of data warehousing concepts and experience with data warehouse design and implementation. ETL processes: Proficiency in designing and implementing Extract, Transform, Load (ETL) processes. Database management systems (DBMS): Extensive knowledge of various DBMS platforms, such as SQL Server, Oracle, and MySQL. Big data technologies: Familiarity with big data technologies like Hadoop, Spark, and NoSQL databases. Business Intelligence: Experience with various BI tools and technologies including Tableau, PowerBI, Microstrategy, etc., Data governance: Understanding of data governance principles and best practices. Cloud computing: Experience with cloud-based data platforms like AWS, Azure, and GCP. Communication and collaboration: Excellent communication and interpersonal skills to effectively collaborate with stakeholders