We are looking for a Senior Data Engineer to join a highly collaborative data team working on modern cloud-native projects with real business impact. Si le interesa solicitar este empleo, por favor, asegúrese de cumplir los siguientes requisitos que se enumeran a continuación. You would be involved in designing and building scalable AWS-based data platforms, working closely with multidisciplinary teams across Data, Product and Business while leveraging modern technologies within the data ecosystem.

What you would be working on:

- Designing and maintaining scalable data pipelines across batch and streaming environments - Building advanced cloud-native data processing solutions within AWS - Translating complex business requirements into robust and scalable technical architectures - Driving Data Quality, observability and reliability initiatives across the platform - Optimising existing processes to improve performance and reduce operational costs - Collaborating closely with different teams to maximise the value and exploitation of data.

Main tech environment





- Python - Spark - AWS (S3, Lambda, API Gateway, EMR, Glue, Athena) - SQL & NoSQL databases - PostgreSQL / MySQL - DynamoDB / MongoDB / DocumentDB - Airflow or AWS Step Functions - Docker & Kubernetes/EKS - Terraform - CI/CD for data platforms - Generative AI applied to development What they are looking for:
- Strong experience building scalable data solutions in AWS - Background working with distributed data processing environments - Someone xhfqzwm proactive with strong analytical thinking and technical ownership - Comfortable collaborating with both technical and business stakeholders - Interest in modern cloud and data architectures such as Lakehouse or Data Mesh Why this opportunity stands out:
- Real business-impact projects - Modern cloud ecosystem across AWS & GCP - Collaborative and dynamic environment - Strong learning and growth opportunities - Adaptable working model with 70% remote

📌 Data Team - Data Engineer (M/F) (Madrid)
🏢 Lawrence Harvey
📍 Madrid