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# Data & AI Engineer

- **Company:** Value Crew
- **Location:** Madrid, Spain (Remote available)
- **Salary:** €50,000.0 - €62,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Cloud Computing, Continuous Integration, Data Validation, Data Systems, Github, Python (Programming Language), Machine Learning, Search Technologies, SQL Databases, Unstructured Data, Data Ingestion, Large Language Models, Apache Spark, Git, Cloudformation, Apache Kafka, Operational Systems, Data Management, Terraform, Data Pipelines, Databricks
- **Published:** September 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=77703759b577c820

## About the Role

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.

## Description

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.