- 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 :

- Strong 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.

- Good 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.

The job is for:

May work from home