DX and Performance TeamWe are looking for a
Staff AI Analytics Engineer
to join our
DX and Performance team
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.
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 Responsibilities
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 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
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
How 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, adaptable approach, while supporting remote work when it makes sense for focus, flexibility, or personal needs.
Benefits
High growth, multicultural and friendly environment
Private health insurance
Wellhub for healthy life (gyms, pools, outdoor classes)
Cobee in‑house savings platform
Language classes
Breakfast in the office and organic fruit
Nora discounts
Free caffeine and theine
Pet friendly environment
#J-18808-Ljbffr

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