Lead Data Engineer

• Design, develop, and implement Spark Scala applications and data processing pipelines to process large volumes of structured and unstructured data.
• Integrate Elasticsearch with Spark to enable efficient indexing, querying, and retrieval of data.
• Optimize and tune Spark jobs for performance and scalability, ensuring efficient data processing and indexing in Elasticsearch.
• Collaborate with data engineers, data scientists, and other stakeholders to understand requirements and translate them into technical specifications and solutions.
• Design and deploy data engineering solutions on OpenShift Container Platform (OCP) using containerization and orchestration techniques.
• Optimize data engineering workflows for containerized deployment and efficient resource utilization.
• Collaborate with DevOps teams to streamline deployment processes, implement CI/CD pipelines, and ensure platform stability.
• Monitor and optimize data pipeline performance, troubleshoot issues, and implement necessary enhancements.

Requirements

• Strong expertise in designing and developing data infrastructure using Hadoop, Spark, and related tools (HDFS, Hive, Ranger, etc)

• Experience with containerization platforms such as OpenShift Container Platform (OCP) and container orchestration using Kubernetes.
• Proficiency in programming languages commonly used in data engineering, such as Spark, Python, Scala, or Java.
• Knowledge of DevOps practices, CI/CD pipelines, and infrastructure automation tools (e.g., Docker, Jenkins, Ansible, BitBucket)
• Experience with Grafana, Prometheus, Splunk will be an added benefit