Staff AI Analytics Engineer

high-performance analytical pipelines using ClickHouse and streaming ingestion with Kafka - Develop and architect custom semantic models and cubes from scratch, defining measures, dimensions, joins, and pre-aggregations - Integrate LLMs into analytics workflows: text-to-SQL, natural-language querying, and conversational BI, ensuring accuracy and governance over results - Apply advanced prompt engineering, tool/function calling, and embedding-based retrieval (RAG over structured data) - Build shared capabilities that will serve as the foundation for other teams to develop intelligent analytical experiences across the platform - Lead architectural decisions around analytical modeling, performance, data governance, observability, and scalability - Collaborate closely with Product, Engineering, Analytics, and Data Science teams to turn complex business questions into scalable, reusable solutions Qualifications & Experience - Strong SQL skills and hands-on experience with ClickHouse (or

equivalent columnar OLAP stores) query optimization, materialized views, and MergeTree engines - Solid grasp of OLAP fundamentals: dimensional modeling, aggregations, and star/snowflake schemas - Proven experience building or defining semantic layers / cubes (e.g. Cube.js) - Experience integrating LLMs into structured data analytics. RAG, text-to-SQL, or tool/function calling - Proficiency in TypeScript for building tools, APIs, and data layer integrations Preferred Experience - Experience working with Ruby on Rails backends (or strong willingness to work within one) - Familiarity with vector databases and embedding-based retrieval systems -

hallucinations. This cross-cutting role has broad impact. You will contribute through hands-on technical work, technical leadership, and by helping teams adopt stronger practices around real-time streaming ingestion, semantic layers, and AI-driven analytics. Factorial serves more than 15,000 active customers and 1 million active users across business-critical workflows. The current environment includes a large Ruby on Rails backend with GraphQL APIs, TypeScript applications and internal tooling, complex CI/CD workflows, MySQL with replicas for OLTP workloads, ClickHouse for analytical workloads, Kafka for event-driven processing and streaming ingestion, a multi-region cloud architecture (AWS/GCP) with Docker/Kubernetes, and modern semantic layers and BI tools (Cube.js, dbt, LookML, Superset, etc.). Key Responsibilities - Lead the evolution of Factorial’s analytics platform, defining how data is transformed into actionable information for millions of users - Design and build