Senior Data Engineer – Python, AWS & ML Pipelines

Lever, Inc.; Portugal; Presencial; EUR 70 000 - 100 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Flexible working arrangements

Career growth opportunities

Training & development

Potential business travel

Collaborative team culture

Resumo da oferta

Lever, Inc. is seeking a Senior Data Engineer based in Portugal to build and operate large-scale data and ML pipelines in production. The role emphasizes Python, ETL, Airflow, and AWS, with collaboration across data engineering, cloud, and ML operations to productionize forecasting.

You will monitor pipeline health, improve reliability, and contribute to architectural decisions within distributed Agile teams.

Qualificações

• Strong experience with Python for data engineering and production systems.
• Proven track record building data or ML pipelines in production.
• Hands-on expertise with Airflow, AWS Step Functions, Glue, and SageMaker.

Responsabilidades

• Build, maintain, and improve production-grade data and ML pipelines.
• Develop ETL and data-processing workflows using Python.
• Design and orchestrate workflows with Airflow and AWS Step Functions.
• Use AWS Glue for data processing and pipeline execution.
• Support deployment and operationalization of ML models using AWS SageMaker.
• Collaborate with Data Scientists and ML Engineers to productionize forecasting.
• Monitor pipeline health and performance, troubleshoot issues, and improve reliability.

Conhecimentos

Python

Data engineering

Machine learning

Distributed systems

Agile collaboration

English communication

Ferramentas

Airflow

AWS Step Functions

AWS Glue

AWS SageMaker

AWS Cloud

GCP

Descrição da oferta de emprego

Lever, Inc. is seeking a Senior Data Engineer based in Portugal to build and operate large-scale data and ML pipelines in production. The role emphasizes Python, ETL, Airflow, and AWS, with collaboration across data engineering, cloud, and ML operations to productionize forecasting.

You will monitor pipeline health, improve reliability, and contribute to architectural decisions within distributed Agile teams.