Data Architect

The Data Architect is responsible for designing and guiding modern data architecture solutions that support business, analytics, and data platform needs. This role focuses on architecting scalable, secure, and high-performing data environments across cloud, hybrid, and on-premises platforms.

The position requires strong expertise in data architecture, platform design, governance, and technical leadership, with the ability to support implementation teams and drive sound architectural decisions. Experience with AI-related architecture concepts is advantageous but not required.

Key Responsibilities

• Lead the design of modern data environments, including data lakes, data warehouses, and lakehouse architectures.
• Define data integration patterns, ingestion frameworks, ETL/ELT processes, and real-time or streaming architectures.
• Develop logical, physical, and conceptual data models aligned to business and analytical requirements.
• Ensure data platforms are designed for scalability, reliability, performance, and cost efficiency.

• Design data solutions across cloud, hybrid, and on-premises environments.
• Define frameworks for storage, compute, security, metadata, and data lineage.
• Recommend architecture approaches, technology options, and best practices aligned with enterprise standards.
• Ensure solutions align with broader architecture principles and governance requirements.

• Support the design and implementation of data governance frameworks and metadata management practices.
• Define approaches for access controls, data quality, cataloging, lineage, and compliance requirements.
• Ensure alignment with enterprise security standards and relevant regulatory requirements.

• Collaborate with engineering and delivery teams to support successful implementation of architecture solutions.
• Provide technical leadership throughout development, integration, and deployment activities.
• Conduct architecture reviews, design validation, and performance optimization assessments.
• Produce and maintain technical documentation, architecture diagrams, and design specifications.

Preferred AI Architecture Exposure

Experience in the following areas is considered an advantage:

• AI and machine learning architecture patterns
• Large language model (LLM), retrieval-augmented generation (RAG), or generative AI solution design
• Data pipelines supporting AI workloads
• Model lifecycle and operational considerations
• AI infrastructure and compute environments

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.
• 5–12 years of experience in data architecture, data platform engineering, or related solution design roles.
• Strong hands-on experience with modern data platforms, including cloud and distributed data environments.
• Deep understanding of ETL/ELT processes, data modeling, data governance, and security principles.
• Strong knowledge of modern data architecture patterns, including lakehouse, warehouse, and integration frameworks.

• Experience working in consulting or systems integration environments.
• Exposure to AI architecture concepts and supporting technologies.

• Data architecture and solution design
• Technical leadership and architecture governance
• Data modeling and integration strategy
• Security and data governance
• Performance optimization and scalability planning

• Modern cloud data platforms
• Data lake, warehouse, and lakehouse architecture
• ETL/ELT and integration pipelines
• Data modeling and metadata management
• Data governance and security frameworks