Snowflake Data Modeler / Data Architect

The Snowflake Data Modeler / Lead Engineer will work closely with Finance and data stakeholders to translate business requirements into detailed Snowflake data and semantic models. The role will lead hands-on implementation of Finance measures and modelling patterns supporting Revenue, Spend and General Ledger use cases, including Snowflake equivalents for the identified GL measures. The role will also establish an AI-ready data foundation, enable model-based analytics, and support agent integration using Snowflake semantic capabilities and the COCO SDK.

The client is particular about cortex flavors, we need candidates who are having experience especially on Cortex AI, Cortex Analysts, Cortex Search, Agentic Development and Semantic modelling.

Candidates MUST have a hands-on experience implementing Snowflake AI features in real-work scenarios. The client is looking for the candidate who can design, build data models, but also utilize Snowflake AI capabilities to create and manage semantic models. And, have the ability to effectively modify and extend semantic models based on evolving business requirements while leveraging Snowflake AI features and tools., 10-15 years of overall experience in data warehousing, data modelling, data engineering or business-intelligence platforms.

• Strong hands-on experience with Snowflake, advanced SQL and enterprise-scale analytical data models.
• Practical experience modelling Finance or regulated-industry data, preferably including GL, P&L, Revenue or Spend reporting.
• Experience designing dimensional models, transformation logic, reusable measures and data-quality controls.
• Experience leading engineering delivery, reviewing technical work and collaborating directly with business and data stakeholders.
• Hands-on experience designing and implementing Snowflake semantic models, semantic layers, governed metrics and reusable business definitions.
• Experience establishing AI-ready data foundations, including metadata, lineage, data quality, governance, security and contextual enrichment for AI consumption.
• Practical experience with model-based analytics and semantic-driven analytical applications.
• Working experience with the COCO SDK or comparable context and agent integration frameworks is strongly preferred.
• Experience integrating AI agents with enterprise data platforms, semantic models, APIs and governed tools, including secure access and observability patterns.

Capability Area

Expected Proficiency

Snowflake engineering

Advanced SQL, dynamic tables, views, performance tuning and workload optimisation

Data modelling

Dimensional, logical and physical modelling; facts, dimensions, aggregates and hierarchies

Finance data

GL, Revenue, Spend, P&L concepts, measures, reconciliation and drill-down

Data pipelines

ETL/ELT patterns, source-to-target mapping, transformation and orchestration

Quality & controls

Profiling, unit testing, reconciliation, lineage, auditability and defect management

Delivery leadership

Technical design, code reviews, mentoring, Agile execution and stakeholder communication