Hybrid Data & MLOps Engineer — GCP, Kubernetes

We are META; Lisboa; Híbrido; EUR 55 000 - 85 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Health insurance

Meal card

Career coach

Flexible work policy

Resumo da oferta

WE ARE META is looking for a Data Engineer / MLOps Engineer to join our team in Lisbon. You will work with a network of national and international tech partners to match consultants with client needs and ensure optimal policy and cultural fit.

You will design and implement data pipelines on GCP (BigQuery focus), leverage Lakehouse/Data Lake architectures, and maintain production-grade deployments with Docker, Kubernetes, and CI/CD (GitHub Actions).

Qualificações

• 3-5 years of experience as a Data Engineer / MLOps Engineer.
• Hands-on experience with Google Cloud Platform, BigQuery, and modern data architectures (Lakehouse/Data Lake).
• Proficiency in Docker, Kubernetes, and CI/CD automation (GitHub Actions or equivalent).
• Experience building and maintaining complex data pipelines (batch & streaming) and API integrations.
• MLOps knowledge including model lifecycle management, packaging, deployment, and monitoring.
• English proficiency at B2/C1 and fluent Portuguese; hybrid Lisbon-based role.
• Must be based in Portugal with Portuguese or EU citizenship, or valid work permit.

Responsabilidades

• Help consultants.find the best matches between our consultants and clients.
• Coordinate hybrid, remote, or on-site work policies to suit preferences and client needs.
• Provide career coaching and networking opportunities through our partner network.

Conhecimentos

Data engineering

MLOps

English proficiency

Ferramentas

Docker

Kubernetes

CI/CD

GitHub Actions

BigQuery

Data Lake

Descrição da oferta de emprego

WE ARE META is looking for a Data Engineer / MLOps Engineer to join our team in Lisbon. You will work with a network of national and international tech partners to match consultants with client needs and ensure optimal policy and cultural fit.

You will design and implement data pipelines on GCP (BigQuery focus), leverage Lakehouse/Data Lake architectures, and maintain production-grade deployments with Docker, Kubernetes, and CI/CD (GitHub Actions).