DX and Performance TeamWe are looking for aStaff AI Analytics Engineerto join ourDX and Performance teamat Factorial. The role is focused on advanced analytical architectures, semantic modeling, and integrating AI/LLM workflows into data consumption, and the right candidate will lead these initiatives.Good candidates think of AI as part of the engineering process, enjoy building products that create real impact for customers, and prioritize solving complex problems over fitting a traditional job description.About the TeamThe team’s primary goal is to increase Factorial’s quality, performance, and scalability by continuously improving how we build our product. We strengthen tools, maintain foundational elements, and promote best practices in close collaboration with 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.Real‑time analytics and intelligent data access are an increasingly important area of focus. As Factorial continues to scale, we strengthen 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.About the RoleYou 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 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 ResponsibilitiesLead the evolution of Factorial’s analytics platform, defining how data is transformed into actionable information for millions of usersDesign and build high‑performance analytical pipelines using ClickHouse and streaming ingestion with KafkaDevelop and architect custom semantic models and cubes from scratch, defining measures, dimensions, joins, and pre‑aggregationsIntegrate LLMs into analytics workflows: text‑to‑SQL, natural‑language querying, and conversational BI, ensuring accuracy and governance over resultsApply 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 platformLead architectural decisions around analytical modeling, performance, data governance, observability, and scalabilityCollaborate closely with Product, Engineering, Analytics, and Data Science teams to turn complex business questions into scalable, reusable solutionsQualifications & ExperienceStrong SQL skills and hands‑on experience with ClickHouse (or equivalent columnar OLAP stores) query optimization, materialized views, and MergeTree enginesSolid grasp of OLAP fundamentals: dimensional modeling, aggregations, and star/snowflake schemasProven 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 callingProficiency in TypeScript for building tools, APIs, and data layer integrationsPreferred ExperienceExperience working with Ruby on Rails backends (or strong willingness to work within one)Familiarity with vector databases and embedding‑based retrieval systemsExperience with cloud environments (AWS/GCP), Docker, and KubernetesFamiliarity with modern BI and data transformation tools such as Cube.Js, dbt, LookML, Metabase, Superset, or TableauHow We WorkWe believe the best products are built when people come together in person to collaborate, challenge ideas, and move fast. That’s why our Engineering teams follow an office‑first, versátil approach, while supporting remote work when it makes sense for focus, flexibility, or personal needs.BenefitsHigh growth, multicultural and friendly environmentPrivate health insuranceWellhub for healthy life (gyms, pools, outdoor classes)Cobee in‑house savings platformLanguage classesBreakfast in the office and organic fruitNora discountsFree caffeine and theinePet friendly environment#J-18808-Ljbffr

📌 Senior Ai Analytics Engineer (Ml & Genai) (Madrid)
🏢 Factorial
📍 Madrid