Senior Data Engineer – GCP

emagine; Lisboa; Híbrido; EUR 55 000 - 90 000; Tempo integral

Resumo da oferta

emagine is seeking a Senior Data Engineer to design, optimize, and operate scalable data platforms in a hybrid environment. You will bridge Data Engineering, DevOps, and MLOps to support data initiatives and analytics.

You’ll implement robust ingestion pipelines, build lakehouse architectures, and promote CI/CD practices using GitHub Actions. Strong GCP and BigQuery skills are essential, as is collaboration with Data Science teams.

Qualificações

• Solid experience in Data Engineering or Software Engineering with large-scale data systems.
• Hands-on experience with Google Cloud Platform (GCP).
• Advanced experience with Google BigQuery and data modelling/best practices.
• Strong understanding of Data Lakes, Lakehouse and layered architectures.
• Designing automated CI/CD pipelines (GitHub Actions).
• Good grasp of DevOps and automated deployment practices.
• Experience building batch and/or streaming data pipelines.
• Professional English proficiency (B2/C1).

Responsabilidades

• Design, develop, monitor, and automate robust data ingestion pipelines for batch and streaming workloads.
• Build integrations with external APIs and third-party data sources, including web scraping.
• Design scalable storage in Google BigQuery with data warehouse best practices.
• Organize data layers from raw ingestion to Publish Layers for analytics.
• Implement and improve CI/CD pipelines ensuring automated deployments.
• Apply software engineering principles to build scalable, secure solutions.
• Support Monorepo workflows enabling collaboration with Data Science teams.
• Assist productionizing Data Science solutions from development to production.
• Contribute to ongoing improvements of the data platform and standards.

Conhecimentos

Data Engineering

Software Engineering

English (B2/C1)

Ferramentas

GCP

BigQuery

Kubernetes

Docker

Datadog

Grafana

Jira

GitHub Actions

VMs

Descrição da oferta de emprego

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.

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

CI/CD: GitHub Actions

Ways of Working: Agile, Scrum, Jira

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

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.