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Key Responsibilities :

- Define and implement enterprise data architecture using Snowflake as the core data platform.
- Design scalable and highly available data warehouse and data lakehouse architectures.
- Develop architectural standards, patterns, frameworks, and best practices for data engineering and analytics platforms.
- Design conceptual, logical, and physical data models aligned with business and analytical requirements.
- Define enterprise data models including dimensional modelling, star schemas, snowflake schemas, fact and dimension structures, and canonical data models.
- Architect data ingestion and integration solutions for structured and semi-structured data from multiple enterprise sources.
- Design batch and near-real-time data pipelines and data processing frameworks.
- Leverage Snowflake capabilities such as Snowpipe, Streams, Tasks, Dynamic Tables, Time Travel, Zero-Copy Cloning, Secure Data Sharing, and Snowflake Marketplace.
- Define strategies for data ingestion, transformation, storage, consumption, and data lifecycle management.
- Establish Snowflake warehouse sizing, workload management, resource utilization, and compute strategies.
- Drive Snowflake performance optimization, query tuning, workload isolation, and cost optimization.
- Define architecture for handling large-scale datasets while ensuring scalability and reliability.
- Establish data security architecture including RBAC, authentication, authorization, masking policies, row-access policies, encryption, and secure data sharing.
- Define data governance, metadata management, data lineage, data quality, and master data management practices.
- Design solutions for data migration from legacy databases and traditional data warehouses to Snowflake.
- Evaluate existing data platforms and recommend modernization and migration strategies.
- Collaborate with data engineers to translate architectural designs into scalable production solutions.
- Review technical designs, data models, pipelines, and implementation approaches to ensure architectural alignment.
- Establish standards for data quality, validation, reconciliation, monitoring, and observability.
- Define disaster recovery, backup, business continuity, and high-availability strategies for critical data workloads.
- Work closely with business stakeholders, product teams, data analysts, BI teams, and technology leadership to understand requirements.
- Provide technical leadership and mentorship to data engineering and development teams.




- Evaluate new Snowflake capabilities and emerging data engineering technologies and recommend their adoption where relevant.
- Create and maintain architecture documentation, solution diagrams, data models, standards, and technical specifications.

Snowflake Expertise :

- Strong understanding of Snowflake architecture, including separation of storage and compute.
- Experience designing multi-database and multi-schema Snowflake environments.
- Strong knowledge of virtual warehouses, warehouse sizing, auto-scaling, workload isolation, and resource management.
- Hands-on understanding of Snowflake Streams, Tasks, Snowpipe, Dynamic Tables, Time Travel, and Zero-Copy Cloning.
- Experience designing secure data-sharing solutions and cross-environment data access.
- Strong understanding of Snowflake security, RBAC, roles, privileges, masking policies, row-access policies, and data protection.
- Experience with Snowflake performance tuning and cost optimization.
- Understanding of Snowflake data loading, unloading, file formats, stages, external tables, and semi-structured data processing.
- Experience architecting data solutions involving high-volume and complex analytical workloads.

Data Engineering & Integration :

- Solid understanding of ETL/ELT architecture and modern data pipeline design.
- Experience with tools such as Apache Airflow, dbt, Informatica, Talend, Azure Data Factory, or similar technologies.
- Strong SQL expertise with experience handling complex transformations and analytical workloads.
- Valuable programming experience with Python or similar languages for data processing and automation.
- Experience integrating data from relational databases, APIs, files, SaaS applications, and enterprise systems.
- Understanding of real-time and event-driven data architectures is an advantage.
- Experience designing reusable and metadata-driven data pipelines.

Data Modelling & Architecture :

- Strong understanding of conceptual, logical, and physical data modelling.
- Expertise in dimensional modelling and analytical data structures.
- Experience designing enterprise data warehouses and modern cloud data platforms.
- Ability to define data domains, subject areas,



data layers, and data consumption patterns.
- Experience with data lake, data warehouse, and lakehouse architectural patterns.
- Understanding of master data, reference data, metadata, and data lineage.
- Ability to balance performance, scalability, maintainability, security, and cost when designing data architectures.

Data Governance & Security :

- Define and implement enterprise data governance frameworks.
- Establish standards for data ownership, classification, quality, lineage, and retention.
- Design secure access models using role-based access control.
- Implement data masking, row-level security, encryption, and controlled data sharing.
- Ensure compliance with organizational security and data protection requirements.
- Establish processes for monitoring data quality and resolving data integrity issues.

Performance & Cost Optimization :

- Analyze Snowflake workloads and identify opportunities for performance improvements.
- Optimize complex queries, warehouse configurations, data models, and transformation processes.
- Design appropriate clustering and partitioning strategies.
- Implement workload isolation and resource management strategies.
- Monitor Snowflake consumption and identify opportunities for reducing compute and storage costs.
- Establish best practices for efficient use of Snowflake resources.

Migration & Modernization :

- Lead migration of legacy data warehouses and databases to Snowflake.
- Assess existing platforms, workloads, data models, and integration patterns.
- Define migration roadmaps, target-state architecture, and migration strategies.
- Develop approaches for data validation, reconciliation, parallel runs, and cutover.
- Modernize legacy ETL processes into scalable cloud-based ELT architectures.

Experience & Qualifications :

- 12 years of experience in data engineering, data architecture, data warehousing, or related roles.
- Strong hands-on experience with Snowflake and enterprise-scale data platforms.
- Proven experience designing and implementing modern data architectures.
- Strong understanding of data modelling, ETL/ELT, data integration, governance, and security.
- Experience leading architecture discussions and providing technical direction to engineering teams.
- Strong problem-solving, communication, stakeholder management, and technical leadership skills.
- Ability to translate complex business requirements into scalable and maintainable data architecture solutions.

📌 Snowflake Data Architect (India)
🏢 Genpact India Private
📍 India