ph3DX and Performance Team /h3 pWe are looking for a bStaff AI Analytics Engineer /b to join our bDX and Performance team /b at 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. /p pGood 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. /p h3About the Team /h3 pThe 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. /p pOur 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. /p pReal‑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. /p h3About the Role /h3 pYou 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. /p pYou 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.



/p pThis 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. /p pFactorial 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.). /p h3Key Responsibilities /h3 ul liLead the evolution of Factorial’s analytics platform, defining how data is transformed into actionable information for millions of users /li liDesign and build high‑performance analytical pipelines using ClickHouse and streaming ingestion with Kafka /li liDevelop and architect custom semantic models and cubes from scratch, defining measures, dimensions, joins, and pre‑aggregations /li liIntegrate LLMs into analytics workflows: text‑to‑SQL, natural‑language querying, and conversational BI, ensuring accuracy and governance over results /li liApply advanced prompt engineering, tool/function calling, and embedding‑based retrieval (RAG over structured data)



/li liBuild shared capabilities that will serve as the foundation for other teams to develop intelligent analytical experiences across the platform /li liLead architectural decisions around analytical modeling, performance, data governance, observability, and scalability /li liCollaborate closely with Product, Engineering, Analytics, and Data Science teams to turn complex business questions into scalable, reusable solutions /li /ul h3Qualifications Experience /h3 ul liStrong SQL skills and hands‑on experience with ClickHouse (or equivalent columnar OLAP stores) query optimization, materialized views, and MergeTree engines /li liSolid grasp of OLAP fundamentals: dimensional modeling, aggregations, and star/snowflake schemas /li liProven experience building or defining semantic layers / cubes (e.g. Cube.js) /li liExperience integrating LLMs into structured data analytics. RAG, text‑to‑SQL, or tool/function calling /li liProficiency in TypeScript for building tools, APIs, and data layer integrations /li /ul h3Preferred Experience /h3 ul liExperience working with Ruby on Rails backends (or strong willingness to work within one) /li liFamiliarity with vector databases and embedding‑based retrieval systems /li liExperience with cloud environments (AWS/GCP), Docker, and Kubernetes /li liFamiliarity with modern BI and data transformation tools such as Cube.js, dbt, LookML, Metabase, Superset, or Tableau /li /ul h3How We Work /h3 pWe 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, adaptable approach, while supporting remote work when it makes sense for focus, flexibility, or personal needs. /p h3Benefits /h3 ul liHigh growth, multicultural and friendly environment /li liPrivate health insurance /li liWellhub for healthy life (gyms, pools, outdoor classes) /li liCobee in‑house savings platform /li liLanguage classes /li liBreakfast in the office and organic fruit /li liNora discounts /li liFree caffeine and theine /li liPet friendly environment /li /ul /p #J-18808-Ljbffr

📌 Staff AI Analytics Engineer (Barcelona)
🏢 Factorial
📍 Barcelona