GCP Data Engineer

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.