- 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
Requirements
- 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)