Senior Data Engineer (Python / AWS / ML Pipelines)

Lever, Inc.; Denmark; On-site; DKK 900,000 - 1,500,000; Full time

Benefits offered by this job

Flexible working

Career growth

Training & mentoring

Healthcare

Travel opportunities

Distributed team collaboration

Job summary

Lever, Inc. is seeking a Senior Data Engineer based in Denmark to build and operate large-scale data and machine learning pipelines in production.

The role emphasizes Python, AWS, ETL, workflow orchestration, and scalable ML infrastructure, with collaboration across Data Scientists and ML Engineers to productionize forecasting and data-driven solutions. You will contribute to reliability, performance, and observability of critical data pipelines, work within distributed Agile teams, and help

Qualifications

• Strong Python experience for data engineering and production systems.
• Proven track record building data and ML pipelines in production.
• Hands-on Airflow for workflow orchestration.
• Experience with AWS Glue and AWS Step Functions.
• Experience deploying ML models with AWS SageMaker.
• Cloud services expertise and production-grade reliability.

Responsibilities

• Build production-grade data and ML pipelines for forecasting and data-driven decisions.
• Develop ETL/data-processing workflows using Python for scalable and reliable pipelines.
• Design and orchestrate workflows with Apache Airflow and AWS Step Functions.
• Utilize AWS Glue for data processing and pipeline execution.
• Deploy and operationalize ML models with AWS SageMaker.
• Collaborate with Data Scientists and ML Engineers to productionize solutions.
• Monitor pipeline health, troubleshoot issues, and improve reliability and performance.
• Contribute to architecture decisions for data platform and ML infra.

Skills

Python

AWS

Airflow

AWS Glue

SageMaker

ML pipelines

Observability

Collaboration

Tools

GCP

Job description

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 Denmark.

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.

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