Magellanic Cloud - Data Architect

What You'll Do :

• Data Governance Leadership : Drive the implementation and adherence to data governance frameworks, policies, and standards, ensuring data quality, privacy, and security across the enterprise.
• Data Architecture Design : Architect scalable, resilient, and high-performance data solutions that meet current and future business needs. This includes designing data lakes, data warehouses, and operational data stores.
• Data Modeling Expertise : Lead the design and development of conceptual, logical, and physical data models for various data initiatives, ensuring data integrity and optimal performance.
• ETL Process Optimization : Design, develop, and optimize efficient ETL (Extract, Transform, Load) processes to ingest, transform, and load large datasets from diverse sources into our data platforms.
• Data Warehousing Strategy : Oversee the strategy, design, and implementation of enterprise data warehousing solutions, ensuring they support advanced analytics and reporting requirements.
• Large Dataset Management : Apply expertise in working with and managing very large datasets, addressing challenges related to volume, velocity, and variety.
• Problem-Solving : Utilize strong analytical and problem-solving skills to diagnose complex data issues and propose effective, sustainable solutions.
• Collaboration & Communication : Collaborate seamlessly with cross-functional teams, including business stakeholders, data scientists, software engineers, and IT operations, effectively communicating complex data concepts and architectural We're Looking For :
• Experience : 10+ years of hands-on experience as a Data Architect or in a similar senior data leadership Skills (Mandatory) :
• Strong expertise in Data Governance principles and implementation.
• Extensive experience in Data Architecture design and strategy.
• Deep proficiency in Data Modeling (conceptual, logical, physical) and database design.
• Hands-on expertise with ETL (Extract, Transform, Load) processes and tools.
• In-depth knowledge of Data Warehousing concepts, design, and implementation.
• Technical Acumen : Proven experience working with large datasets, including performance

tuning and optimization.

articulate complex technical ideas to diverse audiences.

related field.

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