We are looking for a GCP Data Engineer to join a highly technical Data & AI engineering environment, working at the intersection of Software Engineering, Data Engineering and MLOps.

You will design and build scalable data solutions, ML-enabled platforms and event-driven architectures, with a strong focus on engineering quality, automation and cloud-native development.

Your Responsibilities

- Design, develop and operate scalable data pipelines for analytics, reporting and Machine Learning use cases.
- Build data and ML solutions using Python, PySpark, Databricks and MLflow.
- Develop batch, streaming and event-driven architectures within a cloud environment.
- Apply strong Software Engineering principles to Data Engineering, including clean code, testing, maintainability and scalability.
- Optimize the performance and reliability of data engineering solutions.
- Ensure data quality and consistency through validation, monitoring and automated processes.
- Build and maintain CI/CD pipelines for data and software workloads.
- Work primarily across GCP, while contributing to a multi-cloud environment including Azure.
- Collaborate with Data Engineers, Software Engineers, Data Scientists, ML Engineers and Product teams.




- Monitor and troubleshoot production data infrastructure and continuously improve platform reliability.

What We're Looking For
- Strong programming skills in Python, including solid knowledge of Object-Oriented Programming (OOP).
- Hands-on experience with PySpark and/or Databricks.
- Strong SQL skills and experience with platforms such as BigQuery and/or Snowflake.
- Experience designing and developing production-grade data pipelines, including batch and/or streaming workloads.
- Good understanding of GCP and cloud-native data architectures.
- Exposure to multi-cloud environments, ideally GCP and Azure.
- Experience with CI/CD, ideally using Azure DevOps and/or GitLab CI.
- Strong understanding of Software Engineering and DevOps best practices.
- Experience building scalable, maintainable and production-ready data solutions.

Nice to Have
- Experience with MLflow and MLOps practices.
- Docker and Kubernetes.
- Experience with LLMs / Applied AI, including frameworks such as LangChain.
- API development using FastAPI or Flask.
- Experience with event-driven and distributed architectures.
- Knowledge of data security, governance and privacy principles.

📌 GCP Data Engineer (Barcelona)
🏢 Lawrence Harvey
📍 Barcelona