Azure Technology Specialist
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AI Summary
Manage and optimize an AI-powered analytics platform on Snowflake, focusing on semantic layers and prompt engineering to improve AI response accuracy. Perform business analytics and financial modeling to support Pricing, Revenue Operations, and Finance departments.
Position Summary
Support the development, optimization, and ongoing maintenance of the company’s AI-powered analytics platform built on Snowflake. This role combines business analytics, semantic modeling, AI knowledge management, and data modeling to ensure conversational AI tools deliver accurate, reliable, and actionable insights.
Primary Responsibilities
AI & Semantic Layer Management
- Train, evaluate, and continuously improve enterprise AI/NQL models.
- Maintain and optimize semantic models powering conversational analytics.
- Improve AI response accuracy through prompt refinement, metadata management, and business rule configuration.
- Build and maintain business definitions, data relationships, and knowledge layers used by AI agents.
- Test AI-generated responses for quality, consistency, and business accuracy.
- Monitor AI performance and identify opportunities to improve response quality and adoption.
- Assist in developing AI agents within the Snowflake ecosystem.
Snowflake & Data Platform Support
- Support expansion of the Snowflake data platform.
- Validate newly integrated datasets and ensure data quality.
- Assist with onboarding new enterprise systems into Snowflake.
- Document data lineage, business logic, and transformation rules.
- Collaborate with data engineering teams on validation and troubleshooting.
Business Analytics
- Perform ad hoc analysis supporting Pricing, Revenue Operations, Finance, and Operations.
- Develop KPI reporting and executive dashboards.
- Build pricing, forecasting, and operational models.
- Present findings and recommendations.
Excel Modeling
- Develop advanced Excel models for financial and operational analysis.
- Build forecasting, pricing, and scenario planning models.
- Maintain standardized analytical templates.
- Automate repetitive reporting processes.
Data Quality & Governance
- Validate data accuracy across enterprise systems.
- Maintain data dictionaries and business glossaries.
- Identify data quality issues and coordinate resolution.
- Document business rules and calculation methodologies.
Cross-Functional Collaboration
- Partner with business stakeholders to understand requirements.
- Work with Data Engineering, IT, Pricing, Revenue Operations, and Finance.
- Translate business questions into analytical solutions.
- Support user testing and rollout of new AI capabilities.
Preferred Technical Skills
- SQL
- Advanced Microsoft Excel
- Conversational Analytics / NQL
- Semantic Modeling
- AI Prompt Engineering
- Data Modeling
- ETL concepts
- Python
Experience
- 3–6 years in business analytics, analytics engineering, BI, or AI operations.
- Experience supporting enterprise data platforms.
- Experience with semantic layers or business metadata.
- Experience improving AI-generated outputs preferred.
- ERP experience preferred.
- Snowflake experience strongly preferred.
Desired Attributes
- Analytical and detail-oriented.
- Strong problem-solving skills.
- Excellent communication.
- Self-motivated in a remote environment.
- Comfortable working across time zones.
Systems You'll Support
- Snowflake
- Microsoft Great Plains (ERP)
- Motive
- Holman
- HubSpot
- Power BI
- Microsoft Excel
- Internal AI Agents and Conversational Analytics Platform
Nice-to-Have Experience
- Snowflake Cortex AI
- Snowflake Cortex Analyst
- Power BI
- Snowpark
- Semantic Layer Design
- AI Agent Development
- LLMs
- Prompt Engineering
- RAG
- Metadata Management
- Revenue Operations Analytics
- Pricing Analytics
- Fleet Management
- Data Service Operations Analytics
Why This Role Is Strategically Valuable
This position serves as the operational owner of the AI analytics ecosystem, ensuring AI models, semantic definitions, and business knowledge remain accurate as enterprise data grows. The role combines AI operations with traditional business analytics, creating a high-impact resource that scales the organization's AI capabilities.
Requirements
Key Competencies
Education
- Bachelor’s degree in Business, Computer Science, IT, or related field preferred.
- Equivalent work experience in cloud sales/technology will be considered.
- Microsoft Azure Fundamentals (AZ-900) certification is required.
- Advanced Azure certifications such as AZ-104, AZ-305, or equivalent are preferred.
Experience
- 3–5+ years in cloud sales, IT sales, or Microsoft licensing, with proven quota attainment.
- Demonstrated success in building pipeline and closing Azure or equivalent cloud deals.
- Experience co-selling with Microsoft AEs and leveraging Microsoft funding programs.
Knowledge
- Solid understanding of cloud concepts: infrastructure, data, AI, app modernization, security.
- Familiarity with Microsoft licensing models (EA, CSP) and Azure incentive structures.
- Understanding of cloud economics and migration frameworks.
Core Skills
- Strong consultative sales approach with ability to uncover customer needs and map solutions.
- Excellent communication, presentation, and negotiation skills.
- Relationship management with technical and business stakeholders.
- Strategic thinking with ability to connect technical features to business outcomes.
- Problem-solving, objection handling, and multi-tasking.
- Ability to collaborate effectively across internal teams and with Microsoft field resources.
- Autonomous with a strong sense of urgency and execution focus.
- Occasional travel to customer sites, Microsoft events, or team meetings (Canada and U.S.).
- Bilingual (English/French) is an asset but not required.
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