Data Engineer II

📋 Description

• Data Pipeline Operations across multiple sources.
• Design, build, and maintain scalable ETL pipelines.
• Data quality monitoring, validation, and error handling.
• Platform engineering: data models, databases, warehousing.
• Cloud platforms (AWS, GCP, Azure) for data infra.
• Security and governance: data protection compliance.

🎯 Requirements

• Data modeling, database design, and data warehousing.
• SQL proficiency; Python, Java, or Scala.
• Cloud-native architecture: AWS, GCP, or Azure.
• Containerization: Docker and Kubernetes.
• Infrastructure as code: Terraform or CloudFormation.
• 3–5 years in data engineering and data warehousing.
• Knowledge of Flink, Airflow, Spark, or DBT.
• Experience with Kubernetes.