Nimble Gravity believes the right data can transform and propel growth for any organization. They believe that data-driven decision-making partnered with the power of modern technology can accelerate the growth and profitability of almost any business. Backed by Rallyday Partners.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Advanced SQL: CTEs, window functions, incremental pipeline patterns. You can write complex queries without referencing documentation
- 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
- Snowflake Cortex: Cortex Analyst, Cortex Agents, Cortex Search, semantic views, Dynamic Tables
More about this role
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.
This role is focused on the layers that make AI reliable: clean, well-modeled data, governed pipelines, and semantic models that expose business meaning to natural language interfaces. You will design rigorous data models, build data pipelines, and construct the semantic layer that sits between raw data and AI agents. You will be responsible for setting up data that is structured, trusted, and agent-ready. The deployment patterns and data model gaps you surface feed directly back to LOB teams, making you both a practitioner and a source of signal for what gets built next.
- 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...
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