Job Description

We are seeking a talented and experienced Data Engineer (Quantexa)with expertise in Hadoop, Scala, Spark, Elastic, Open Shift Container Platform (OCP) and DevOps practices. Elasticsearch to join our team. As a Data Engineer, 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. Knowledge and experience in the Compliance / AML domain will be a plus. Working experience with Quantexa tool is a must.
Responsibilities:
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 * More than 5 years of experience as a Data Engineer

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related discipline
• Possession of Quantexa certification as a Data Engineer or Data Architect, with proficiency in the tool
• Demonstrated experience as a Data Engineer, utilizing Hadoop, Spark, and data processing technologies in large-scale environments
• Expertise in the Scala programming language and familiarity with functional programming principles
• Prior experience with the Quantexa tool is highly desirable
• Comprehensive understanding of Apache Spark architecture, including RDDs, DataFrames, and Spark SQL
• Advanced proficiency in designing and developing data infrastructure utilizing Hadoop, Spark, and associated tools (HDFS, Hive, Pig, etc.)
• Experience with containerization platforms such as OpenShift Container Platform (OCP) and container orchestration via Kubernetes
• Proficiency in programming languages commonly employed in data engineering, including Spark, Python, Scala, or Java
• Knowledge of DevOps methodologies, CI/CD pipelines, and infrastructure automation tools (e.g., Docker, Jenkins, Ansible, BitBucket)
• Experience with Graphana, Prometheus, and Splunk will be considered an added advantage
• Background in integrating and utilizing Elasticsearch for data indexing and search applications
• Solid understanding of Elasticsearch data modeling, indexing strategies, and query optimization techniques
• Experience with distributed computing, parallel processing, and handling large datasets
• Proficient in performance tuning and optimization methods for Spark applications and Elasticsearch queries
• Strong problem-solving and analytical capabilities with the capacity to debug and resolve intricate issues
• Familiarity with version control systems (e.g., Git) and collaborative development environments

Skills Required