Data & AI Engineer

As a Data & AI Engineer, you’ll build and operate the data systems behind both analytics and AI use cases.

This isn’t a role where you’ll spend your time moving CSV files around or building dashboards. You’ll work on production pipelines, cloud infrastructure, data quality, orchestration and the datasets that ML and AI services consume.

You’ll work closely with Data Engineers, AI Engineers, Product and Platform Engineering, and you’ll own pieces of the platform from design through production.

What you’ll own

• Design, build and maintain reliable batch and event-driven data pipelines.
• Build reusable data products that serve analytics, ML and AI workloads.
• Work with structured and unstructured data coming from APIs, operational systems, IoT sources and internal applications.
• Develop production-grade services and pipelines using Python and SQL.
• Build and evolve cloud infrastructure using services such as S3, Lambda, Glue, RDS and Step Functions.
• Manage infrastructure through Terraform or CloudFormation.
• Build CI/CD workflows with GitHub Actions.
• Introduce data quality checks, observability, lineage and sensible alerting.
• Help create datasets for ML models, semantic search and retrieval-based AI features.
• Investigate production issues and improve reliability instead of treating operations as somebody else’s problem.
• Work with Product and AI teams to turn ambiguous requirements into pragmatic technical solutions.

Tech environment

You don’t need to know every tool below on day one.

Core: Python, SQL, AWS, Git, CI/CD Data: S3, Glue, RDS, Spark, orchestration Infrastructure: Terraform / CloudFormation, GitHub Actions Observability: logs, metrics, data-quality monitoring AI-facing data: embeddings, vector-ready datasets, RAG ingestion pipelines, Experience with Spark or Databricks, Airflow, Kafka, dbt, vector databases, ML feature pipelines or data platforms supporting LLM applications.

You’ll probably have around 3+ years of professional engineering experience, although we’re more interested in what you’ve actually built than the number on your CV.

We’d like to see:

• Strong hands-on Python and SQL.
• Experience building data pipelines that run in production.
• Practical cloud experience, ideally AWS.
• Solid understanding of data modelling and data quality.
• Experience with CI/CD and version-controlled engineering workflows.
• An understanding of reliability: retries, idempotency, monitoring, failure recovery and cost.
• Comfortable working independently on a problem while asking for context when you need it.
• English good enough to work with an international engineering team.

• €50k-62k gross annual salary, depending on experience.
• Permanent employment contract.
• Remote-first setup anywhere in Spain.
• Flexible working hours.
• Optional access to offices in Barcelona and Madrid.
• Home-office budget.
• Private health insurance.
• Learning budget for courses, conferences or certifications.
• 25 working days of annual leave.
• Regular engineering meetups without turning office attendance into presenteeism.

More importantly, you’ll join at the point where the company is building its next generation of data infrastructure. There’s enough scale to work on real engineering problems, but not so much bureaucracy that changing something requires six committees.

Client: European B2B Technology Scale-up

Build the data foundations that AI products actually depend on.

We are working with a European B2B technology company building software for asset-intensive businesses. Its platform connects operational, IoT and business data to help customers reduce energy consumption, prevent downtime and make better day-to-day decisions.

The company has grown beyond the early start-up phase, with more than 300 people across several European markets, real enterprise customers and a product handling an increasingly complex mix of structured, semi-structured and real-time data.

Now the Data & AI team is moving from analytics-focused infrastructure towards a platform capable of supporting machine learning and AI-powered product features at scale.