Responsibilities
- Architect and enhance analytical data solutions natively within Snowflake.
- Develop standardized, reusable datasets for financial domains such as positions, transactions, cash flows, performance, P&L, and risk.
- Utilize Snowflake features including Dynamic Tables, Streams, Tasks, Secure Views, RBAC, data sharing, and optimization techniques for cost and workload management.
- Transform core banking platform data structures into business-intuitive analytical models.
- Apply modern data modeling techniques such as dimensional modeling and medallion or data-product-centric architectures.
- Establish uniform metrics, KPIs, and analytical domains that support multi-client reuse.
- Ensure data assets are easily discoverable, thoroughly documented, and trusted across teams.
- Implement governance frameworks in Snowflake using masking policies, row-level security, object tagging, and access controls.
- Embed data quality, security, and trust principles directly into platform design.
- Prepare the data environment to support large-scale AI initiatives.
- Enable AI and advanced analytics using Snowflake Cortex, Snowpark ML, vector search, and document intelligence capabilities.
- Prioritize AI operationalization and integration over experimental data science.
- Support intelligent automation, analytics, and decision-making across banking functions.
- Serve as a technical expert and reference point for Snowflake within the engineering team.
- Collaborate with Product Managers, Architects, Analysts, and Engineers across global locations.
- Assist in proof of concepts, client onboarding, and production deployments.
- Lead the development of documentation, technical standards, and best practices for the data platform.
Work Arrangement
Hybrid
