Data and Analytics Engineer

About the role

Analytics Engineer & AI Specialist

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.

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.

What You'll Work On

Data Modeling and Architecture

- 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

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

- IMP

- 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

- 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.