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Hybrid remote, common data engineer title but specialized Databricks/Spark/Kafka requirements reduce applicant density.
Specialized Databricks/Spark/Kafka skills transferable but favor enterprise/cloud backgrounds.
Mandatory 10+ years and many required technologies (Databricks, Spark, Kafka, cloud, Kubernetes) create strict filters.
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Design, develop, and maintain scalable data pipelines and enterprise-grade data solutions using Databricks, Apache Spark, and Kafka.
Deploy and manage real-time streaming data architectures and data platforms on Azure and/or AWS cloud environments.
Collaborate with cross-functional teams to implement microservices architecture and maintain data quality, governance, security, and performance optimization.
10+ years of experience in Data Engineering or related fields.
Strong hands-on expertise in Databricks, Delta Lake, Apache Spark, Apache Kafka, Python, SQL and NoSQL databases.
Experience with cloud platforms such as Azure and/or AWS, and container orchestration using Kubernetes (K8S).
Strong knowledge of Java development and microservices architecture.
Experienced in designing and supporting large-scale, enterprise-grade, cloud-native data platforms with modern data lake architectures.
Proficient in building real-time streaming and event-driven data solutions integrating Databricks, Spark, Kafka, and Kubernetes.
Comfortable working in collaborative, cross-functional environments delivering high-impact, scalable data engineering projects.