Snowflake Cortex Semantic & Agent Engineer

Snowflake Cortex Semantic & Agent Engineer Role Overview

We are seeking a hands-on Snowflake Cortex Semantic & Agent Engineer to build Snowflake Cortex Skills, agent capabilities, reusable skill packages and tools for governed Finance AI use cases. The engineer will implement Talk-to-Data experiences, connect agents to approved semantic models and business context, and improve response quality using business questions, verified queries, interaction patterns and identified response gaps.

Required Experience

• 8-12 years of experience in software engineering, AI engineering, data engineering, analytics engineering, semantic modelling or enterprise application development.
• Hands-on experience with Snowflake, SQL and the development of data-driven applications or analytical solutions.
• Mandatory hands-on experience with Snowflake Cortex semantic models, including business entities, relationships, dimensions, measures, metrics, verified queries and semantic grounding.
• Mandatory working knowledge of the COCO SDK, including its use for enterprise context, semantic assets or agent integration.
• Experience building AI-ready data foundations with governed, discoverable and context-rich data products for agentic and analytical use cases.
• Experience implementing model-based analytics using reusable semantic definitions, governed measures and calculation models.
• Hands-on experience integrating AI agents with semantic models, enterprise data, APIs, tools and services using secure and observable patterns.
• Experience developing in Python and integrating REST APIs, JSON-based services and enterprise systems.
• Experience testing AI or data solutions using repeatable datasets, expected results and automated quality checks.
• Strong troubleshooting, code-quality, documentation and Agile delivery skills.
• Prior experience in Finance, banking, capital markets, wealth management or regulated reporting is preferred.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, Data Science or a related discipline.
• SnowPro Core or another relevant Snowflake certification is preferred.
• Experience with enterprise chat, business-intelligence or conversational analytics integrations is advantageous.
• Deep experience with semantic modelling, metadata platforms, governed data products and model-based analytics is strongly preferred.
• Experience delivering AI capabilities within a regulated enterprise environment is preferred.

Success Measures

• Cortex Skills and agents answer prioritised Finance questions using governed data and approved business context.
• Response accuracy and coverage improve through systematic analysis of verified queries, interaction patterns and response gaps.
• Reusable tools and skill packages reduce duplication and accelerate additional Finance AI use cases.
• Evaluation, regression and confidence controls provide clear evidence of agent quality and behavioural changes.
• Delivered capabilities meet agreed security, performance, reliability, documentation and production-readiness standards.

Core Technical & Domain Skills

Capability Expected expertise Snowflake Cortex Cortex Skills, Cortex Agents, verified queries, semantic grounding and Talk-to-Data

Agentic AI Agent workflows, tools, prompts, context management, orchestration and reusable skill packages

Development Python, advanced SQL, REST APIs, JSON, Git and CI/CD fundamentals

AI quality Golden datasets, evaluation, regression testing, confidence scoring, grounding and feedback loops

Cortex semantic modelling (Mandatory)

Cortex semantic models, entities, relationships, dimensions, measures, metrics, verified queries and semantic grounding

Security & operations Entitlements, data protection, prompt-injection awareness, monitoring, logging and runbooks

Finance awareness GL, P&L, Revenue, Spend, financial measures or management reporting preferred

COCO SDK (Mandatory) Working knowledge of COCO SDK for enterprise context, semantic asset and agent integration

AI-ready data foundation Governed data products, metadata, lineage, quality, entitlements, discoverability and contextual enrichment

Model-based analytics Reusable semantic definitions, governed calculations, Finance measures and consistent analytical outcomes

Agent integration Secure integration with Cortex, semantic models, enterprise APIs, tools and services; orchestration and observability