Analytics Engineer & AI Specialist

As Analytics Engineers you would combine insurance domain expertise with full-stack data and analytics engineering capabilities. You will help build the data foundations that power Snowflake's AI platform.

What You'll Work On

Data Modeling and Architecture

- Architect versátil, performant data models that drive LOB team toward single sources of truth across their LOB 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
- Design Data security so that the model only has access to the data

Semantic Layer and Agent Readiness

- Build semantic data models that expose LOBs data to natural language queries via Cortex Analyst, turning complex schemas into something a business stakeholder and Clients can ask a question of
- Define and validate the metrics, dimensions, and relationships that AI agents need to reason correctly over LOBs data
- Identify and resolve gaps in data structure, naming, and coverage that would cause an agent to fail or produce incorrect results

Documentation

- Documented playbooks, reusable data model templates, and semantic model libraries that can be maintained and extend
- Run technical workshops to upskill other team members
- Author semantic view configurations and skill files (YAML + Markdown) that a non-technical analyst can invoke in plain English

Hard Skills Required Must-Have

- CTEs, window functions,



incremental pipeline patterns. You can write complex queries without referencing documentation.

Analytics Engineering And Data Modeling

- Experience building data infrastructure involving large-scale relational datasets; strong instincts for pipeline design, QA, and testing across the full stack from ingestion through semantic layer.

Dbt

- Experience building and maintaining dbt projects with testing, documentation, and CI/CD pipelines.

Python

- Modern, type-hinted, readable. You understand Python-based data pipelines and automation workflows.

AI-assisted Development

- You have used an LLM coding assistant (CoCo, Cursor, GitHub Copilot, Claude, or equivalent) as your primary development environment. Daily usage is the baseline.

Semantic Modeling

- You can write a semantic view configuration or structured skill file that handles edge cases and encodes enough domain knowledge that the model behaves like a subject matter expert.

Client-facing Communication

- You write code, but your output needs to make sense to a business leader who has never opened a terminal.
- You are the translation layer between what Snowflake's AI can do and what the customer actually needs.

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, identifies stall points, and re-engages until the data product is reliable and can be handed over to run teams.
- Codifies, Doesn't Customize
- Instinct is to turn patterns into reusable templates and playbooks that the next engineer can deploy at the next customer, not to build bespoke every time.
- Comfortable With Ambiguity
- Engages with customers to derive requirements, prototypes fast, gathers feedback, and iterates.
- Signal Clarity
- Distills messy deployments into clean, actionable feedback for Leaders, explaining root causes and suggesting fixes, not just reporting problems.

Minimum Requirements

- 8+ 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
- Has shipped 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

Why This Role Is Different

Most analytics engineering roles stop at the data mod

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