ML Data Engineer — Build Scalable AI-Ready Pipelines

Capgemini Engineering; Lisboa; Híbrido; EUR 48 000 - 70 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Health and life insurance

Hybrid work environment

Referral program with bonuses

Resumo da oferta

Capgemini Engineering is seeking a data engineer to join a multidisciplinary Data Science team focused on recommendation products for residential and SME customers. Your mission is to consolidate existing processes into a cross-domain framework and enable ML/AI capabilities through robust data foundations.

You will design, build and maintain scalable data pipelines with Python, PySpark and SQL, orchestrate Airflow DAGs, implement CI/CD with GitHub, and ensure data quality, observability and SLA

Qualificações

• Degree in Computer Science, Engineering, Mathematics, Statistics or a quantitative field.
• At least 3 years of experience building and operating production-grade data pipelines.
• Strong knowledge of Python and/or PySpark, as well as SQL.

Responsabilidades

• Consolidate recommendation processes across B2C and SME domains into a unified data framework.
• Design, build and maintain scalable data pipelines using Python, PySpark and SQL.
• Orchestrate workflows using Airflow DAGs deployed through Astronomer.
• Implement automated testing, deployment and CI/CD processes using GitHub.
• Ensure data quality, validation, observability and compliance with SLAs.
• Monitor production pipelines, manage alerts and automate recurring activities.

Conhecimentos

Python

PySpark

SQL

Airflow

GitHub

Distributed computing

Data quality

ML workflows

Data science familiarity

Ownership

Formação académica

Bachelor's degree in CS/Engineering/Math/Stats

Ferramentas

Airflow

BigQuery/Dataproc

dbt/Great Expectations

Descrição da oferta de emprego

Capgemini Engineering is seeking a data engineer to join a multidisciplinary Data Science team focused on recommendation products for residential and SME customers. Your mission is to consolidate existing processes into a cross-domain framework and enable ML/AI capabilities through robust data foundations.

You will design, build and maintain scalable data pipelines with Python, PySpark and SQL, orchestrate Airflow DAGs, implement CI/CD with GitHub, and ensure data quality, observability and SLA