• Data Strategy and Architecture: Develop and implement a comprehensive data strategy and architecture that aligns with the organization's business goals, leveraging best practices and emerging technologies.
• Data Modeling: Design and oversee the development of data models, ensuring data is structured for efficient storage, retrieval, and analysis.
• Data Integration: Lead efforts to integrate data from diverse sources, both internal and external, to create a unified and accurate view of data, leveraging tools like ETL pipelines and data integration platforms.
• Data Warehousing: Design, build, and manage data warehousing solutions, ensuring data is stored in a secure, scalable, and accessible manner.
• Cloud Data Solutions: Architect data solutions on cloud platforms such as Google Cloud, AWS, or Azure, utilizing cloud-native services for data storage, processing, and analytics.
• Big Data Technologies: Utilize big data technologies and frameworks, like Hadoop and Spark, to process and analyze large volumes of data efficiently.
• Data Security and Compliance: Ensure data security and compliance with industry regulations by implementing robust security measures and data governance practices.
• Data Quality Assurance: Implement data quality controls and validation processes to maintain high data accuracy and reliability.
• Data Governance: Establish data governance frameworks, policies, and procedures to manage data assets effectively and ensure data is used responsibly.
• Data Cataloging and Metadata Management: Implement data cataloging solutions and metadata management practices to enable data discovery and lineage tracking.
• Data Access and APIs: Create data access APIs and enforce access controls to ensure data is securely accessible to authorized users and applications.
• Scalability and Performance: Optimize data solutions for scalability and performance, ensuring responsiveness and efficiency, especially during high-demand periods.
Mandatory : Excellent Communication as it involves Client Management
