Staff AI Analytics Engineer

Hello! 👋 Would you love to improve the way companies take care of their people, their most valuable asset? We are looking for an Staff AI Analytics Engineer to join our DX and Performance team at Factorial, with a strong focus on advanced analytical architectures, semantic modeling, and integrating AI/LLM workflows into data consumption, and who want to lead that change people who: Think of AI as part of their engineering process, not a feature they bolt on Get energy from building products and seeing them generate real impact for customers Care more about solving complex problems than fitting a traditional job description Ready to take on the challenge? 🚀 DX and Performance Team 🛠️

The team’s primary goal is to increase Factorial’s quality, performance, and scalability by continuously improving the way we build our product. We focus on strengthening our tools, maintaining foundational elements, and promoting best practices in close collaboration with the rest of the engineering organization. Our mission is to equip product builders and business teams with robust, AI-enabled tools and practices to deliver and interpret insights with quality, confidence, and efficiency. We work across teams to improve analytical software patterns, optimize high-scale data queries, and raise the overall data and engineering bar across the company. Within this context, real-time analytics and intelligent data access are an increasingly important area of focus. As Factorial continues to scale, we want to strengthen how we handle massive-scale data ingestion, build highly efficient OLAP structures, and apply modern LLM workflows to bridge the gap between complex databases and natural language querying.

The role 📜 As an Engineer at Factorial, you’ll be part of a team of 200+ Engineers. We look for people who are curious, proactive, technically strong, and effective communicators. People who enjoy solving complex technical problems, raising standards across teams, and building systems that help the whole organization perform at a higher level. In this role, you will help shape how Factorial processes, models, and queries high-performance analytical data. You will work closely with product and data teams to design semantic models from scratch, optimize complex columnar data stores, and build modern text-to-SQL or conversational BI capabilities that govern how users interact with data. You will partner with engineering leaders across product and infrastructure to build robust data pipelines and ensure our AI integrations are grounded, accurate, and secure against hallucinations. This is a cross-cutting role with 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. You’ll work in a multicultural, English-speaking environment where your technical depth, systems thinking, and problem-solving skills will directly contribute to our mission of improving business management processes for companies worldwide. Factorial serves 15,000+ active customers and 1M+ active users across business-critical workflows. Our 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 (using MergeTree engines, partitioning, and materialized views) Kafka for event-driven processing and streaming ingestion A multi-region cloud architecture (AWS/GCP) and Docker/Kubernetes Modern semantic layers and BI tools (Cube.js, dbt, LookML, Superset, etc.) Don’t let unfamiliarity with every part of this stack hold you back. If you have worked in complex data systems, know how to reason about performance at scale, and are excited to help teams build next-generation AI-driven analytics, we want to talk to you. What you will work on Lead the evolution of Factorial's analytics platform, defining how data is transformed into actionable information for millions of users Design and build high-performance analytical pipelines using ClickHouse and streaming ingestion with Kafka Develop and architect custom semantic models and semantic cubes from scratch defining measures, dimensions, joins, and pre-aggregations, not just configuring off-the-shelf frameworks 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 We are looking for profiles that have 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 / semantic 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 Ideally, you also bring Experience working with Ruby on Rails backends (or a strong willingness to work within one). Familiarity with Vector Databases and embedding-based retrieval systems. Experience with Cloud environments (AWS/GCP), Docker, and Kubernetes. Familiarity with modern BI and data transformation tools (such as Cube.js, dbt, LookML, Metabase, Superset, or Tableau).