Senior Data Engineer (Python / AWS / ML Pipelines)
New
J
JobgetherData engineering
Based in BelgiumFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Excellent written and verbal English communication skills.
- Required Skills
- AWSPythonApache AirflowETLData engineering
Requirements
- Have strong professional experience with Python applied to data engineering and production systems.
- Have proven experience building and maintaining data pipelines and/or machine learning pipelines in production.
- Have hands-on experience with Apache Airflow for workflow orchestration.
- Have practical experience with AWS Step Functions and AWS Glue.
- Have experience deploying and supporting machine learning models using AWS SageMaker.
- Have strong hands-on experience with AWS cloud services and cloud-based production environments.
- Have experience with systems operating at significant scale and understand reliability and performance considerations.
- Be able to collaborate with Data Scientists, ML Engineers, and other technical stakeholders.
- Have excellent written and verbal English communication skills.
- Be comfortable working independently and collaboratively within distributed, cross-functional teams.
- Experience with GCP is a plus but not required.
- Familiarity with monitoring and observability tools is advantageous.
Responsibilities
- Build, maintain, and improve production-grade data and machine learning pipelines supporting forecasting and data-driven decision-making.
- Develop scalable, reliable, and maintainable ETL and data-processing workflows using Python.
- Design and orchestrate workflows using Apache Airflow and AWS Step Functions.
- Use AWS Glue for data processing, transformation, and pipeline execution.
- Support deployment and operationalization of machine learning models using AWS SageMaker.
- Work with Data Scientists and ML Engineers to move models and analytical solutions into production.
- Monitor pipeline health and performance, troubleshoot production issues, and improve reliability, scalability, and efficiency.
- Contribute to architectural and technical decisions related to the data platform, ML infrastructure, and production workflows.
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