Data Engineer (Quantexa)

Posted 13 OctFound 20 OctSeen 1 hour ago

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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

Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field
Must be Quantexa certified data engineer / data architect and proficient with the software.
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.
In-depth understanding of Apache Spark architecture, RDDs, DataFrames, and Spark SQL
Strong expertise in designing and developing data infrastructure using Hadoop, Spark, and related tools (HDFS, Hive, Pig, 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 Graphana, Prometheus, Splunk will be an added benefit
Experience integrating and working with Elasticsearch for data indexing and search applications
Experience with Quantexa tool is highly preferred.
In-depth understanding of Apache Spark architecture, RDDs, DataFrames, and Spark SQL
Strong expertise in designing and developing data infrastructure using Hadoop, Spark, and related tools (HDFS, Hive, Pig, etc)