- Expertise in data warehousing concepts, database management (SQL/NoSQL), ETL tools (e.g., Informatica, Talend, Azure Data Factory), and cloud platforms (e.g., AWS, Azure, GCP).

- Data Modelling : Strong knowledge of dimensional modelling, star schema, and snowflake schema design.

- Data Governance : Knowledge of data privacy regulations (GDPR, HIPAA) and governance frameworks.

- Communication : Ability to translate complex technical concepts into business-friendly terms.

- Problem-Solving : Strong analytical skills to troubleshoot data and performance issues effectively.

Key Responsibilities :

1. Data Architecture Design :

- Design the overall architecture for the enterprise data warehouse, ensuring scalability, performance, and data integrity.

- Define standards and best practices for data modelling, storage, and access patterns.

- Develop logical and physical data models, data flow diagrams, and database design that support business needs.

2. ETL Process Development :

- Lead the design and development of ETL (Extract, Transform, Load) processes to integrate data from various sources into the data warehouse.

- Establish ETL workflows that ensure efficient, error-free, and high-performance data extraction and transformation.

- Optimize ETL processes for maximum performance and minimal downtime during data loads.

3. Data Integration and Transformation :

- Ensure the smooth integration of structured and unstructured data from disparate systems, including on-premises and cloud-based solutions.

- Define data transformation rules to clean, normalize, and enrich data before loading it into the warehouse.

- Oversee the integration of real-time and batch data pipelines for continuous data ingestion.

4. Data Quality and Governance :

- Establish data quality standards and implement processes to ensure the accuracy, consistency, and reliability of data.

- Collaborate with the data governance team to define and enforce data governance policies across the organization.

- Implement monitoring and validation systems to detect and resolve data anomalies or discrepancies.

5. Collaboration with Stakeholders :

- Work closely with business analysts, data scientists, and BI developers to understand their data requirements and align the architecture to support these needs.

- Partner with IT teams to ensure seamless integration between data systems and support application development initiatives.

- Act as a liaison between technical teams and business stakeholders to communicate data architecture and capabilities.

6. Performance Tuning and Optimization :

- Continuously monitor data warehouse performance and implement strategies to optimize query performance, ETL jobs, and system scalability.

- Proactively identify bottlenecks in ETL processes or data warehouse queries and resolve them to improve overall efficiency.

7. Security and Compliance :

- Ensure data security and privacy by implementing robust access control mechanisms, encryption, and auditing procedures in compliance with regulatory requirements.

- Define and enforce security policies that restrict unauthorized access to sensitive data.

8. Documentation and Standards :

- Maintain comprehensive documentation of the data warehouse architecture, ETL processes, and data integration workflows.

- Establish coding standards, naming conventions, and documentation best practices to ensure consistency and maintainability.