Overview

In this StaffAI Analytics Engineer role, you will help shape Factorial’s analytics platform with a strong emphasis on advanced analytical architectures, semantic modeling, and AI/LLM-powered data workflows. You’ll work within the DX and Performance team to improve real-time analytics, data access, and scalable pipelines that empower product and business teams. This position offers the chance to influence architectural decisions and drive AI-enabled analytics across the company. You’ll join a mission to deliver robust, high-quality insights at scale.

Compensaciones / Incentivos

- Alan private health insurance
- Wellhub (fitness benefits)
- Cobee expense management
- Language classes
- Breakfast in the office and organic fruit
- Pet Friendly

Responsabilidades

- Lead evolution of Factorial's analytics platform and how data becomes actionable for millions of users
- Design high-performance pipelines using ClickHouse and Kafka-based streaming
- Define and architect semantic models/cubes (measures, dimensions, joins, pre-aggregations)
- Integrate LLMs into analytics workflows (text-to-SQL, natural-language querying, conversational BI) with governance
- Apply prompt engineering, tool calling, and embedding-based retrieval (RAG)




- Build shared capabilities to enable other teams to develop intelligent analytic experiences
- Lead architectural decisions around modeling, performance, governance, observability, and scalability
- Collaborate with Product, Engineering, Analytics, and Data Science to translate business questions into scalable solutions

Requisitos principales

- Strong SQL skills with hands-on ClickHouse experience (queries, materialized views, MergeTree)
- Solid OLAP fundamentals (dimensional modeling, aggregations, star/snowflake schemas)
- Experience defining/building semantic layers/cubes (e.g. Cube.js)
- Experience integrating LLMs into structured analytics (RAG, text-to-SQL, tool calling)
- Proficiency in TypeScript for tooling, APIs, and data integrations
- Willingness to work with Ruby on Rails backends (optional)
- Familiarity with cloud environments (AWS/GCP), Docker, Kubernetes
- Experience with BI and data transformation tools (Cube.js, dbt, LookML, Superset)

- Curiosity and proactive problem-solving
- Effective communication across cross-functional teams
- Strong collaboration with Product, Engineering, Analytics, and Data Science
- ClickHouse (OLAP store)
- Kafka (streaming ingestion)
- TypeScript

📌 Staff AI Analytics Engineer - barcelona
🏢 Factorial Hr
📍 Barcelona