Data Engineer, Experimentation Platform

Super; Madrid; Presencial; EUR 65.000 - 90.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Medical / Health Insurance

Open Annual Leave

Employee Assistance Programme

Training & Learning Development

Descripción de la vacante

Super is seeking a Data Engineer to own the data backbone of our Experimentation Platform across Europe. You will work on the Metric Store, evaluation pipelines, and deploy services end-to-end using Docker, Kubernetes, and GitHub Actions.

Collaborate with backend engineers, data scientists, and product managers to ensure trustworthy experiments, data quality, and scalable pipelines. Strong Python, FastAPI, Airflow, Snowflake, and SQL are essential.

Formación

• Strong Python engineering skills with production-ready code.
• Experience with API development (FastAPI or similar) and sound software design.
• Proficient with a modern data stack: workflow orchestration (Airflow), cloud data warehouse (Snowflake), and SQL.

Responsabilidades

• Own and evolve the Metric Store and its API services.
• Build and maintain evaluation and monitoring pipelines in Airflow on Snowflake.
• Deploy services end to end with Docker, Kubernetes, and CI/CD tooling.
• Investigate data quality issues and reconcile metrics across data sources.
• Collaborate with backend engineers, data scientists, and product managers; review experiments and RFCs.
• Improve developer experience with documentation and tooling.

Conocimientos

Python engineering

FastAPI

Airflow

Snowflake

SQL

CI/CD

Kubernetes

Observability

Data quality

Communication

Herramientas

Docker

GitHub Actions

Prometheus

Grafana

Descripción del empleo

Super is seeking a Data Engineer to own the data backbone of our Experimentation Platform across Europe. You will work on the Metric Store, evaluation pipelines, and deploy services end-to-end using Docker, Kubernetes, and GitHub Actions.

Collaborate with backend engineers, data scientists, and product managers to ensure trustworthy experiments, data quality, and scalable pipelines. Strong Python, FastAPI, Airflow, Snowflake, and SQL are essential.