Role Overview
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 Will Do
Design rigorous data models, build data pipelines, and construct the semantic layer that sits between raw data and AI agents.
Why It Might Be a Fit
You will be responsible for setting up data that is structured, trusted, and agent-ready, and provide input into data governance strategies including permissions, data lineage, and data definitions.
Requirements
- Advanced SQL: CTEs, window functions, incremental pipeline patterns.
- 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.
- 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.
Benefits
- Dental insurance
- Vision insurance
- Health insurance
- Paid time off
- Retirement plan
- Learning budget
- Parental leave
- Wellness programs
To apply for this job please visit job-boards.greenhouse.io.
Follow us on social media