Data Engineer

Summary

Join Xebia, a global leader in digital solutions, and contribute to the development and deployment of machine learning systems for e-commerce and other platforms. You will work alongside data scientists and analysts, building scalable and efficient data processes, writing efficient software, and promoting best practices. This role requires extensive experience in machine learning, data engineering, and cloud technologies, particularly within the Google Cloud Platform (GCP). The ideal candidate possesses strong programming skills (Python), experience with various tools and technologies (Spark, Airflow, Azure DevOps), and excellent communication skills. Xebia offers a collaborative environment focused on professional development and innovation.

Requirements

• 4+ years of experience developing and deploying machine learning systems into production
• 8+ years of experience as a data engineer or software developer
• Strong programming skills like Python
• Experience with Spark Cluster, Airflow, Azure DevOps, CI/CD pipelines, and containerization
• Experience in Spark3+, MLFlow, containerization and Python, Scikit-learn, Pandas, NumPy
• Hands-on experience with Affinity with Advanced Analytics, Data Science, NLP
• Deep understanding of batch and streaming data (S3, Spark, Kafka, Flink)
• Experience in building complex ETL
• Expertise in SQL and noSQL
• Good understanding of RDBMS, non-SQL and time-series databases
• Experience with Google Cloud Platform (GCP) and managed GCP services (GKE, GCS, BQ, Dataproc, Dataflow)
• Deep knowledge of public Cloud Analytics
• Monitoring, observability, logging, alerting
• Very good verbal and written communication skills in English
• Work from the European Union region and a work permit are required

Responsibilities

• Work with data scientists and analysts to create and deploy new product features on the e-commerce website, in-store portals, and clients’ mobile apps
• Establish scalable, efficient, automated processes for data analysis, model development, validation, and implementation
• Write efficient and scalable software to ship products in an iterative, continual-release environment
• Contribute to and promote good software engineering practices across the team and building cloud-native software for ML pipelines
• Contribute to and reuse community best practices
• Develop, train and deploy machine learning models
• Build a real-time Fraud Analytics systems

Preferred Qualifications

• Experience with Go, Java or Scala
• Expertise in managed Azure or AWS services
• Experience in Kubernetes