Data Warehouse Engineer

Job Description

A Data Warehouse Developer designs, builds, and maintains data warehouses—central repositories where organizations store and manage large volumes of structured and sometimes unstructured data. These warehouses serve as the foundation for business intelligence, reporting, and data analysis. Here are the key aspects of the role:

Design and Architecture:

• Data Modeling: Develop data models that represent the structure of the warehouse. This involves understanding business requirements, creating logical and physical data models, and ensuring efficient data storage.
• ETL (Extract, Transform, Load): Design ETL processes to extract data from various sources (such as databases, APIs, or flat files), transform it into the desired format, and load it into the data warehouse.
• Schema Design: Define schemas (e.g., star schema, snowflake schema) to organize data for optimal querying and reporting.

Data Integration:

• ETL Development: Write ETL scripts or workflows using tools like SSIS (SQL Server Integration Services), Informatica, or Talend.
• Data Cleansing: Ensure data quality by cleaning and transforming raw data during the ETL process.
• Data Loading: Load data into the warehouse tables efficiently.

Performance Optimization:

• Indexing: Create appropriate indexes to speed up query performance.
• Partitioning: Partition large tables to enhance query efficiency.
• Materialized Views: Use materialized views or summary tables for pre-aggregated data.

• Reporting and Analytics:

• Business Intelligence (BI): Collaborate with BI developers to create reports, dashboards, and visualizations.
• SQL Queries: Write complex SQL queries to retrieve data for analysis.
• OLAP (Online Analytical Processing): Understand OLAP concepts for multidimensional analysis.

• Data Security and Compliance:

• Access Control: Define user roles and permissions to restrict access to sensitive data.
• Compliance: Ensure compliance with data privacy regulations (e.g., GDPR, HIPAA).

• Monitoring and Maintenance:

• Performance Monitoring: Monitor data warehouse performance, identify bottlenecks, and optimize resource usage.
• Backup and Recovery: Implement backup and recovery strategies to prevent data loss.
• Data Refresh: Schedule regular data updates and refreshes.

• Collaboration and Communication:

• Business Stakeholders: Work closely with business analysts, data scientists, and other stakeholders to understand their data needs.
• IT Teams: Collaborate with database administrators, system administrators, and network engineers.

Qualifications and Skills:

• Technical Skills:
• Proficiency in SQL (Structured Query Language) for querying and managing data.
• Familiarity with ETL tools (e.g., SSIS, Informatica, Talend).
• Understanding of data modeling concepts.
• Problem-Solving Abilities:
• Analytical mindset to optimize performance and troubleshoot issues.
• Ability to handle large datasets efficiently.
• Communication Skills:
• Explain complex technical concepts to non-technical stakeholders.
• Document processes and best practices.