Data Engineer (AI)

TL;DR

Data Engineer (AI): Building and deploying ML models that automate content creation and power a smarter product assortment, with an accent on resilient ETL/ELT pipelines to ingest, clean, and validate image data and associated metadata. Focus on building reliable, production-grade feature pipelines and automated infrastructure to support the Data Scientist's model training cycles.

Location: Onsite in Barcelona, Spain

Company

Glovo, part of the Delivery Hero Group, is a multi-category app in Spain connecting millions of users with businesses and couriers.

What you will do

• Design and build resilient ETL/ELT pipelines to ingest, clean, and validate image data and associated metadata.
• Work with the Data Scientist to create and maintain the "Ground Truth" dataset for calibrating the third-party model and benchmarking the accuracy.
• Build reliable, production-grade feature pipelines to ensure that data used during model training is identical to the data used during live inference.
• Build the automated infrastructure to support the Data Scientist's model training cycles, focusing on data versioning and reproducibility.
• Contribute to the long-term vision of products image recognition, while delivering short-term wins that unlock measurable business impact.

Requirements

• Bachelor's degree in computer science, information systems, mathematics, statistics, or a related field.
• 3+ years of professional experience as a Data Engineer or Machine Learning Engineer.
• Advanced knowledge of SQL and distributed processing frameworks.
• Excellent engineering skills: write clean, maintainable Python code.
• Strong systems-level problem-solving skills, with the ability to balance performance, scalability, maintainability, and business impact.
• Excellent communication skills, with the ability to clearly articulate complex technical problems and solutions to both engineers and business stakeholders.

Nice to have

• PhD in AI, Machine Learning, or a related field.
• Experience on Computer Vision (CV).
• Experience bringing ML models into production with best practices for observability, monitoring, and performance.
• Experience with CI/CD pipelines and commonly used tools in ML Engineering such as Metaflow, MLflow, Airflow, Grafana, and similar.
• Experience with large-scale ML model validation, experimentation, and A/B testing.
• Hands-on experience with major cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).

Culture & Benefits

• Monthly Glovo credits.
• Discounted gym memberships.
• Extra time off, freedom to work from home two days a week, and the opportunity to work from anywhere for up to three weeks a year.
• Enhanced parental leave, and office-based nursery.
• Online therapy and wellbeing benefits.