As a Data Engineer,
Your main responsibilities will include:
- 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.
Requirements
- Bachelor's degree in Computer Science, Data Engineering, Information Technology, or a related field.
- At least 4 plus years of experience as a Data Engineer, working with Hadoop, Spark, and data processing technologies in large-scale environments.
- 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)
