Cloud ETL Architect

We are seeking an experienced and highly skilled Cloud ETL Architect to play a foundational leadership role within our Digital Transformation (DT) Data Practice. This role goes beyond traditional pipeline design and development. The ideal candidate will define enterprise-scale data architecture, lead hyperscaler-based solutioning, support RFPs and proposals, engage with clients, and establish strong technical governance across complex delivery programs.

Roles & Responsibilities :Architecture & Strategy :

• Define and implement enterprise data architecture strategies aligned with business and digital transformation objectives.

• Design scalable cloud-based ETL/ELT and data integration architectures.

• Develop data modernization roadmaps, including migration from legacy/on-premise systems to cloud platforms.

• Evaluate and recommend appropriate technologies, frameworks, and architectural patterns.

• Define architecture standards, best practices, and reusable frameworks for enterprise data platforms.

Data Engineering & Cloud Platforms :

• Architect robust data ingestion, transformation, orchestration, and processing pipelines.

• Design solutions across leading cloud platforms such as AWS, Microsoft Azure, and Google Cloud Platform.

• Work with modern data technologies including data lakes, lakehouses, data warehouses, and distributed processing frameworks.

• Provide technical leadership on ETL/ELT tools, APIs, streaming platforms, and batch processing solutions.

• Ensure scalability, performance optimization, reliability, and cost efficiency across data platforms.

BI, Analytics & Semantic Layer :

• Design data architectures that support enterprise BI, advanced analytics, and reporting requirements.

• Define semantic layers, curated data models, and data consumption frameworks.

• Collaborate with analytics and business intelligence teams to ensure data platforms are optimized for reporting and decision-making.

• Support integration with visualization and BI tools such as Power BI, Tableau, Looker, or similar platforms.

Data Quality, Governance & Security :

• Define enterprise data quality frameworks, validation mechanisms, and monitoring standards.

• Implement data governance principles including metadata management, lineage, cataloging, and master data considerations.

• Ensure compliance with enterprise security standards and data privacy regulations.

• Design role-based access controls, encryption, auditing, and secure data access mechanisms.