Job
Description
Knowledge and experience in the Compliance / AML domain will be a plus,Working experience with Quantexa tool is a must,Proven experience as a Data Engineer, working with Hadoop, Spark, and data processing technologies in large-scale environments Proficiency in Scala programming language and familiarity with functional programming concepts Experience with Quantexa tool is highly preferred,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,Experience with Graphana, Prometheus, Splunk will be an added benefit Experience integrating and working with Elasticsearch for data indexing and search applications Solid understanding of Elasticsearch data modeling, indexing strategies, and query optimization Experience with distributed computing, parallel processing, and working with large datasets Proficient in performance tuning and optimization techniques for Spark applications and Elasticsearch queries Strong problem-solving and analytical skills with the ability to debug and resolve complex issues Familiarity with version control systems Elasticsearch to join our team,you will play a crucial role in designing, developing, and optimizing big data solutions using Apache Spark, Scala, and Elasticsearch,You will collaborate with cross-functional teams to build scalable and efficient data processing pipelines and search applications,Implement data transformation, aggregation, and enrichment processes to support various data analytics and machine learning initiatives Collaborate with cross-functional teams to understand data requirements and translate them into effective data engineering solutions 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 Implement data transformations, aggregations, and computations using Spark RDDs, DataFrames, and Datasets, and integrate them with Elasticsearch Develop and maintain scalable and fault-tolerant Spark applications, adhering to industry best practices and coding standards Troubleshoot and resolve issues related to data processing, performance, and data quality in the Spark-Elasticsearch integration Monitor and analyze job performance metrics, identify bottlenecks, and propose optimizations in both Spark and Elasticsearch components Ensure data quality and integrity throughout the data processing lifecycle 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 Implement data governance practices, data lineage, and metadata management to ensure data accuracy, traceability, and compliance Monitor and optimize data pipeline performance, troubleshoot issues, and implement necessary enhancements Implement monitoring and logging mechanisms to ensure the health, availability, and performance of the data infrastructure Document data engineering processes, workflows, and infrastructure configurations for knowledge sharing and reference Requirements
