Data Warehouse Design and Implementation: They design, build, and maintain data warehouses and data lakes, optimizing them for analytical queries and reporting. This includes selecting appropriate architectures (e.g., Kimball, Inmon), ETL (Extract, Transform, Load) processes, and data modeling techniques.
Big Data Platform Management: They manage and administer big data platforms like Hadoop, Spark, and Hive, ensuring they are scalable, reliable, and secure. This involves cluster management, resource allocation, and performance tuning.
Data Integration: They integrate data from various sources (structured and unstructured, internal and external) into the data warehouse or data lake, ensuring data quality and consistency.
Performance Optimization: They optimize the performance of big data systems and data warehouses for complex analytical queries, using techniques like indexing, partitioning, and query optimization.
Data Governance and Security: They implement data governance policies and security measures to protect sensitive data within the data warehouse and big data environment. This includes access control, data masking, and compliance with regulations like GDPR.
Data Modeling for Analytics: They design data models optimized for analytical queries, enabling business users to easily access and analyze data.
Tool Selection and Evaluation: They evaluate and recommend appropriate big data and data warehousing tools and technologies, staying abreast of the latest advancements in the field.
Collaboration: They work closely with data scientists, analysts, and business stakeholders to understand their data needs and provide support for data-driven initiatives.