Senior Data Engineer – GCP

Are you an experienced Data Engineer passionate about building scalable data platforms, automating complex pipelines, and working at the intersection of Data Engineering, DevOps, and MLOps ?

We’re looking for a technically strong and proactive Senior Data Engineer to join a growing data engineering environment. You’ll play a key role in designing, optimizing, and maintaining the infrastructure and pipelines that support data initiatives, Data Science projects, and analytical operations .

This is a great opportunity for someone who enjoys combining strong data engineering expertise with cloud, automation, and software engineering best practices , while helping bridge the gap between model development and scalable production environments.

🚀 What You’ll Be Doing:

• Design, develop, monitor, and automate robust data ingestion pipelines , covering both batch and streaming workloads .
• Build and maintain complex integrations with external APIs and third-party data sources , including application-level data extraction and web scraping scenarios.
• Design and organize scalable data storage solutions within Google BigQuery , following strong data modelling and Data Warehouse best practices.
• Structure and maintain clear data layers, from raw ingestion environments (Data Lake / Data Swamp) through to optimized Publish Layers for analytics and downstream consumption.
• Implement and improve CI/CD pipelines , ensuring reliable, automated, and repeatable deployments.
• Apply strong software engineering principles to build scalable, secure, maintainable, and well-documented solutions.
• Help structure and maintain modern repositories, including Monorepo environments , enabling efficient collaboration between Data Engineering and Data Science teams.
• Support the transition of Data Science solutions from development into production-ready environments .
• Contribute to the continuous improvement of the overall data platform, engineering standards, automation, and development practices.

🔎 What We’re Looking For:

Must-Have:

• Solid professional experience in Data Engineering or Software Engineering , particularly with large-scale data systems.
• Strong hands-on experience with Google Cloud Platform (GCP) .
• Advanced practical experience with Google BigQuery , including data modelling and Data Warehouse best practices.
• Strong understanding of modern data architectures, including Data Lakes, Lakehouse concepts, and layered data architectures .
• Experience designing and maintaining automated CI/CD pipelines , ideally using GitHub Actions or equivalent technologies.
• Good understanding of DevOps principles and automated deployment practices.
• Experience building and maintaining robust batch and/or streaming data pipelines .
• Professional proficiency in English (B2/C1) , both written and spoken, for collaboration in an international technical environment.

⭐ Nice to Have:

• Hands-on experience with MLOps concepts and tools , including model lifecycle management, model API serving, and Feature Stores.
• Experience deploying solutions in Kubernetes or other container orchestration environments.
• Experience with Docker and Virtual Machines (VMs) .
• Knowledge of observability and monitoring solutions such as Datadog or Grafana .
• Experience working in Agile/Scrum environments and using tools such as Jira .
• Previous experience supporting Data Science teams and helping move models or data products into production.

🛠 Tech Stack:

Cloud & Data: Google Cloud Platform (GCP), Google BigQuery

Data Architecture: Data Lake, Lakehouse, Data Warehouse, Raw Layers, Publish Layers

DevOps & Infrastructure: Docker, Kubernetes, Virtual Machines

CI/CD: GitHub Actions

Observability: Datadog, Grafana

Ways of Working: Agile, Scrum, Jira

Additional Areas: MLOps, APIs, Batch & Streaming Pipelines, Monorepo

📍 Work Model:

This position follows a hybrid working model, with 2 days per week at the office .

If you’re looking for an opportunity where you can combine Data Engineering, GCP, DevOps, and MLOps while contributing to the evolution of a modern and scalable data platform, we’d love to hear from you.