Data Python Engineer
Technology
Bucharest, RomâniaAstăziPână la 13.12.2026
Normă întreagăHibrid
Descrierea postului
Data Engineer (Python)
At Capgemini Engineering, the world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world’s most innovative companies unleash their potential. From autonomous cars to life-saving robots, our digital and software technology experts think outside the box as they provide unique R&D and engineering services across all industries. Join us for a career full of opportunities, where you can make a difference and where no two days are the same.Your Role
As a Data Engineer, you will contribute to the development and evolution of a modern data platform that supports large-scale data collection, processing, analytics, and operational intelligence. Working in a collaborative Agile environment, you will design, build, and optimize data solutions that enable reliable, high-quality data delivery across business and engineering teams.In this role you will play a key role in:
- Designing, building, and maintaining scalable ETL/ELT pipelines and data workflows
- Developing and supporting both batch and real-time streaming data solutions
- Improving platform reliability, monitoring, observability, and overall performance
- Building integrations between internal systems, cloud services, external platforms, and connected IoT devices
- Collaborating with software engineers, analysts, and platform teams to troubleshoot production issues and deliver new data capabilities
- Participating in technical design discussions, automation initiatives, CI/CD pipelines, and engineering best practices to ensure high-quality data services
- Strong hands-on experience with Python, including development of production-grade software, data pipelines, APIs, and automation solutions
- Experience designing and implementing ETL/ELT pipelines and scalable data processing solutions
- Good knowledge of SQL, data modeling, query optimization, and relational databases
- Experience with cloud-based data platforms and modern data architectures (AWS, Azure, or GCP)
- Hands-on experience with Apache Airflow or similar workflow orchestration platforms
- Experience working with Kafka or other event-driven and streaming technologies
- Strong Linux fundamentals and experience supporting production environments
- Familiarity with CI/CD practices, automated testing, Git workflows, and modern software delivery processes
- Strong debugging, troubleshooting, and problem-solving capabilities
- Ability to work independently and collaborate effectively within cross-functional teams
- Experience with ClickHouse, Redshift, Snowflake, or other data warehousing technologies
- Experience with Spark / PySpark
- Experience with Terraform, Infrastructure as Code, and cloud automation
- Knowledge of Docker and Kubernetes
- Experience supporting large-scale production data platforms
- Experience with AI/ML data pipelines, feature stores, or machine learning infrastructure
- Knowledge of data governance, data quality frameworks, and metadata management
- Impact:Work on a modern data platform that processes and delivers critical data across multiple business domains, enabling data-driven decisions, operational efficiency, and future AI initiatives.
Career Growth:Benefit from structured career paths, with opportunities to expand your expertise across data engineering, cloud technologies, platform engineering, analytics, and emerging AI/ML ecosystems.
Learning &
- Development:Access continuous learning programs, technical training, certifications, and hands-on experience with leading data technologies, cloud platforms, and industry best practices.
- Support:Join a collaborative network of data engineers, software engineers, architects, and technology professionals who value knowledge sharing, mentorship, and technical excellence.
- Balance:Hybrid working policies allow you to manage your time and your work base effectively while maintaining a healthy work-life balance.
Keywords
PythonETL/ELT PipelinesSQLData ModelingCloud Data PlatformsApache AirflowKafkaLinuxCI/CDAutomated TestingGitData StreamingTroubleshootingData WarehousingSpark/PySparkTerraformData EngineeringETL/ELTData PipelinesAPIs
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