Senior Data Engineer (Python / AWS / ML Pipelines)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Engineer (Python / AWS / ML Pipelines) based in Saudi Arabia. As a Senior Data Engineer, you will build and operate large-scale data and machine learning pipelines in a production environment. You will work across data engineering, cloud infrastructure, and machine learning operations to turn models into reliable production workflows. The role focuses heavily on Python, AWS, ETL, workflow orchestration, and scalable ML infrastructure. You will collaborate closely with Data Scientists and ML Engineers to productionize forecasting and data-driven solutions. You will also help improve the reliability, performance, and observability of critical data pipelines. Working within distributed Agile teams, you will contribute to architecture decisions and technical improvements across the data platform. This is an opportunity to work on challenging production systems while continuing to develop your cloud and ML engineering expertise. Accountabilities • Build, maintain, and improve production-grade data and machine learning pipelines supporting forecasting and data-driven decision-making. • Develop ETL and data-processing workflows using Python, ensuring they are scalable, reliable, and maintainable. • Design and orchestrate workflows using technologies such as Apache Airflow and AWS Step Functions. • Use AWS Glue for data processing, transformation, and pipeline execution. • Support the deployment and operationalization of machine learning models using AWS SageMaker. • Work closely with Data Scientists and ML Engineers to move models and analytical solutions reliably into production. • Monitor pipeline health and performance, troubleshoot production issues, and implement improvements to reliability, scalability, and efficiency. • Contribute to architectural and technical decisions related to the data platform, ML infrastructure, and production workflows. • Collaborate effectively with distributed, cross-functional Agile teams and contribute ideas that improve engineering practices and delivery. Requirements • Strong professional experience with Python and its application to data engineering and production systems. • Proven experience building and maintaining data pipelines and/or machine learning pipelines in production. • Hands-on experience with Apache Airflow for workflow orchestration. • Practical experience with AWS Step Functions and AWS Glue. • Experience deploying and supporting machine learning models using AWS SageMaker. • Strong hands-on experience with AWS cloud services and cloud-based production environments. • Experience working with systems operating at significant scale, with a strong understanding of reliability and performance considerations. • Experience collaborating closely with Data Scientists, ML Engineers, and other technical stakeholders. • Strong troubleshooting, analytical, and problem-solving abilities. • Excellent written and verbal English communication skills. • 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 also advantageous. Benefits • Collegial working environment where responsibility and decision-making are shared across the team. • Agile culture where employees are encouraged to contribute ideas and influence technical direction. • Supportive approach to learning from mistakes and continuously improving ways of working. • Opportunities to work on different projects and broaden your technical experience. • Ongoing training, mentoring, and professional development. • Opportunities for career growth and exposure to new technologies and challenges. • Possibility of business travel. • Flexible working arrangements appropriate to a distributed team. • Additional salary, healthcare, and other employment benefits may vary according to local terms in Slovenia. How Jobgether Works We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. Show more Show less

Strong professional experience with Python and its application to data engineering and production systems. Proven experience building and maintaining data pipelines and/or machine learning pipelines in production. Hands-on experience with Apache Airflow for workflow orchestration. Practical experience with AWS Step Functions and AWS Glue. Experience deploying and supporting machine learning models using AWS SageMaker. Strong hands-on experience with AWS cloud services and cloud-based production environments. Experience working with systems operating at significant scale, with a strong understanding of reliability and performance considerations. Experience collaborating closely with Data Scientists, ML Engineers, and other technical stakeholders. Strong troubleshooting, analytical, and problem-solving abilities. Excellent written and verbal English communication skills. 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 also advantageous.

Build, maintain, and improve production-grade data and machine learning pipelines supporting forecasting and data-driven decision-making. Develop ETL and data-processing workflows using Python, ensuring they are scalable, reliable, and maintainable. Design and orchestrate workflows using technologies such as Apache Airflow and AWS Step Functions. Use AWS Glue for data processing, transformation, and pipeline execution. Support the deployment and operationalization of machine learning models using AWS SageMaker. Work closely with Data Scientists and ML Engineers to move models and analytical solutions reliably into production. Monitor pipeline health and performance, troubleshoot production issues, and implement improvements to reliability, scalability, and efficiency. Contribute to architectural and technical decisions related to the data platform, ML infrastructure, and production workflows. Collaborate effectively with distributed, cross-functional Agile teams and contribute ideas that improve engineering practices and delivery.