Responsibilities
- Define and own the end-to-end data architecture for Snowflake-based data warehousing and analytics solutions, aligned with enterprise standards and best practices.
- Design scalable data models, schemas, and data pipelines to support reporting, self-service analytics, and AI/ML workloads.
- Architect and oversee the implementation of ELT/ETL frameworks, data ingestion patterns, and integration with diverse source systems (batch and real-time).
- Lead modernization of legacy data warehouses to Snowflake, including migration strategy, performance optimization, and cost management.
- Establish data automation strategies, including orchestration, monitoring, and CI/CD for data pipelines and data products.
- Collaborate with AI/ML teams to design data platforms that support feature stores, model training, and model inference at scale.
- Define and implement data governance, data quality, security, and access control frameworks within Snowflake and related tools.
- Provide architectural guidance and technical leadership to data engineers, developers, and analysts across projects.
- Conduct architecture reviews, PoCs, and technology evaluations for data engineering, automation, and AI-enabling tools and platforms.
- Optimize Snowflake performance, storage, and compute usage through clustering, partitioning, caching, and workload management.
- Develop and maintain architecture blueprints, reference implementations, and reusable patterns for data warehousing and analytics.
- Partner with product owners and business stakeholders to translate analytical and AI requirements into robust data solutions.
- Ensure solutions are secure, compliant, resilient, and aligned with organizational policies and regulatory requirements.
- Mentor and upskill team members on Snowflake, data engineering best practices, and data automation techniques.
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