Data Engineer — Data Platform
Posted Updated
Summary
Data Engineer building, operating, and optimizing production data pipelines and the underlying data platform in Budapest, using Python, Airflow, Kubernetes, and PostgreSQL. Focus areas include pipeline scalability, testing, monitoring, CI/CD, data quality, and evaluating tools like Apache NiFi.
- Build, operate, and improve production data pipelines using Python, Airflow, Kubernetes, and related technologies
- Design scalable data pipelines and data models for growing data volumes and processing needs
- Improve testing, monitoring, observability, data quality, CI/CD, and documentation across data workflows
- Evaluate and introduce suitable processing tools, including flow-based systems such as Apache NiFi
- Optimize PostgreSQL schemas, queries, and data access patterns
- Improve existing systems incrementally while maintaining reliable production operation
- Collaborate with ML engineers, data scientists, product teams, and other engineers
- Take ownership of technical problems from design through deployment and operation
- Contribute to the ongoing evolution of our data platform and architecture
- Professional experience building and operating production data pipelines
- Strong Python skills and experience writing maintainable production code
- Hands-on experience with workflow orchestration tools such as Apache Airflow or comparable systems
- Good understanding of data pipeline and orchestration design
- Working knowledge of PostgreSQL or similar relational databases, including schema design and query performance
- Experience with Docker and Kubernetes
- Experience improving and maintaining existing production systems
- Familiarity with testing, monitoring, data quality, and CI/CD practices
- Strong problem-solving skills and the ability to work autonomously within an agreed technical direction
- Experience with Apache NiFi or other flow-based or dataflow tools
- Experience designing or modernizing data platforms and architectures
- Experience with geospatial, GIS, LiDAR, sensor, remote sensing, or large-scale scientific data
- Experience with cloud infrastructure and distributed data processing
- Experience in startup or scale-up environments
- Experience in mentoring or technical leadership
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