Forward Deployed Analytics Engineer & AI Specialist

Data Modeling and Architecture

• Architect flexible, performant data models that drive customers toward single sources of truth across their key business domains
• Use SQL, Python, dbt, and Snowflake to build and maintain data infrastructure for reporting, analysis, and automation
• Perform data QA and develop automated testing procedures for Snowflake data models
• Provide input into data governance strategies including permissions, data lineage, and data definitions

Semantic Layer and Agent Readiness

• Build semantic data models that expose customer tables to natural language queries via Cortex Analyst
• Define and validate the metrics, dimensions, and relationships that AI agents need to reason correctly over customer data
• Identify and resolve gaps in data structure, naming, and coverage that would cause an agent to fail or produce incorrect results

Enablement and Knowledge Transfer

• Build documented playbooks, reusable data model templates, and semantic model libraries
• Run technical workshops to upskill customer data and analytics teams on Snowflake's AI development environment
• Author semantic view configurations and skill files (YAML + Markdown)

Hard Skills Required

Must-Have:

• Advanced SQL: CTEs, window functions, incremental pipeline patterns
• Analytics engineering and data modeling: Experience building data infrastructure involving large-scale relational datasets
• Python: Modern, type-hinted, readable
• AI-assisted development: Daily usage of an LLM coding assistant (CoCo, Cursor, GitHub Copilot, Claude, or equivalent)
• Semantic modeling: Write semantic view configurations or structured skill files
• Client-facing communication: Translate technical output for business stakeholders

Strong Plus:

• dbt: Experience building and maintaining dbt projects with testing, documentation, and CI/CD pipelines
• Snowflake Cortex: Cortex Analyst, Cortex Agents, Cortex Search, semantic views, Dynamic Tables
• Experience with Airflow or other orchestration frameworks
• Familiarity with enterprise business systems (ERP, CRM, HRIS, or similar)

Soft Skills Required

• Owns the outcome: Tracks adoption after go-live and re-engages until the customer's data is reliable
• Codifies, doesn't customize: Turns patterns into reusable templates and playbooks
• Comfortable with ambiguity: Engages with customers to derive requirements, prototypes fast, gathers feedback, and iterates
• Signal clarity: Distills messy customer deployments into clean, actionable feedback for product and research teams

Minimum Requirements

• 5+ years of experience in analytics engineering, data engineering, or a related technical role, with at least a portion of it customer-facing or cross-functional
• Daily use of an AI coding assistant as a primary development tool
• Proficient in SQL; can write window functions and complex joins without referencing documentation
• Experience with dbt or equivalent data modeling framework
• Has shipped at least one production data model or pipeline that non-technical business users actually relied on
• Comfortable in Git (PRs, branches, code review)
• Demonstrable experience translating business requirements into technical specifications

What Success Looks Like at 90 Days

• Engaged in at least two customer engagements, with measurable data quality or semantic layer improvements
• Built at least one semantic model that a customer's non-technical users can query in plain English via Cortex Analyst
• Identified and resolved at least one upstream data quality or modeling issue that was blocking an AI use case
• Filed at least three product feedback items that the Cortex product team has engaged with