Data and Analytics Engineer

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.

• Architect flexible, 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

• 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
• IMP
• Identify and resolve gaps in data structure, naming, and coverage that would cause an agent to fail or produce incorrect results

• 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

• Advanced SQL: 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).

• 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.

• 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

Most analytics engineering roles stop at the data model. Most field roles stop at the recommendation. This role st