Hybrid ML & Data Platform Engineer — Airflow & Data Mesh

Capgemini; Lisboa; Híbrido; EUR 48 165 - 65 679; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Hybrid work

Health and life insurance

Referral program

Resumo da oferta

Capgemini Engineering in Lisbon looks for a Machine Learning Engineer to consolidate holistic data foundations for ML products. You will design and operate scalable data pipelines with Python, PySpark and SQL, and orchestrate workflows using Airflow deployed through modern CI/CD practices.

Collaboration with data scientists and stakeholders is essential to evolve ML-ready data solutions. The role emphasizes data quality, observability, and governance, with opportunities to contribute across data

Qualificações

• Degree in Computer Science, Engineering, Mathematics or a quantitative field.
• 3+ years building and operating production-grade data pipelines.
• Strong Python, PySpark and SQL knowledge with data-focused mindset.
• Experience with Airflow and CI/CD, ideally GitHub-based workflows.
• Familiarity with distributed computing and large-scale data processing.

Responsabilidades

• Consolidate B2C and SME recommendation data into a unified data framework.
• Design, build and maintain scalable data pipelines using Python, PySpark and SQL.
• Orchestrate workflows with Airflow DAGs and deploy via CI/CD processes.
• Ensure data quality, validation and observability with data contracts and tests.
• Monitor pipelines, manage incidents and automate recurring tasks.
• Collaborate with Data Scientists and Product Owners to translate ML/AI needs into data solutions.
• Document architectures and data flows; evolve data and ML engineering practices.

Conhecimentos

Python

PySpark

SQL

Airflow

CI/CD

GitHub

Data quality

Observability

Data modeling

ML workflows

Formação académica

Bachelor's degree in Computer Science or related

Ferramentas

Airflow

GitHub

BigQuery

Dataproc

dbt

Great Expectations

Descrição da oferta de emprego

Capgemini Engineering in Lisbon looks for a Machine Learning Engineer to consolidate holistic data foundations for ML products. You will design and operate scalable data pipelines with Python, PySpark and SQL, and orchestrate workflows using Airflow deployed through modern CI/CD practices.

Collaboration with data scientists and stakeholders is essential to evolve ML-ready data solutions. The role emphasizes data quality, observability, and governance, with opportunities to contribute across data