Staff AI Analytics Engineer

Factorial; Barcelona; Híbrido; EUR 60.000 - 80.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Private health insurance

Wellhub for healthy life

Language classes

Breakfast in the office

Pet friendly environment

Descripción de la vacante

Factorial is seeking a Staff AI Analytics Engineer to join the DX and Performance team. In this role, you will shape how Factorial processes and models analytical data, design semantic models, and lead initiatives integrating AI and data workflows.

The ideal candidate will have strong SQL skills, experience with ClickHouse, and proficiency in TypeScript, working collaboratively across product and engineering teams to deliver impactful data solutions.

This position promotes a flexible, office-first working approach with various benefits including health insurance and wellness programs.

Formación

• Hands-on experience with ClickHouse query optimization and materialized views.
• Solid grasp of OLAP fundamentals: dimensional modeling and aggregations.
• Proven experience building or defining semantic layers.

Responsabilidades

• Lead the evolution of Factorial’s analytics platform.
• Design and build high-performance analytical pipelines.
• Integrate LLMs into analytics workflows.

Conocimientos

Strong SQL skills

Query optimization

Proficiency in TypeScript

Experience with ClickHouse

Integration of LLMs into data analytics

Herramientas

ClickHouse

Docker

Kubernetes

Descripción del empleo

DX and Performance Team

We 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 Team

The 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 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 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 Work

We 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, flexible 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