Role Overview
We are seeking a talented and experienced Data Engineer with strong expertise in Quantexa, Hadoop, Scala, Apache Spark, Elasticsearch, OpenShift Container Platform (OCP), and DevOps practices.
The successful candidate will be responsible for designing, developing, optimizing, and maintaining scalable big data solutions using Apache Spark, Scala, Hadoop, and Elasticsearch. The role involves collaborating with cross-functional teams to build efficient data processing pipelines and search applications.
Knowledge and experience in the Compliance / AML domain will be an added advantage.
Key Responsibilities
- Design, develop, and implement Spark/Scala applications and data processing pipelines for large volumes of structured and unstructured data.
- Implement data transformation, aggregation, enrichment, and computation processes to support analytics and machine learning initiatives.
- Collaborate with cross-functional teams to understand data requirements and translate them into effective data engineering solutions.
- Integrate Elasticsearch with Spark for efficient data indexing, querying, and retrieval.
- Implement transformations and aggregations using Spark RDDs, DataFrames, Datasets, and Spark SQL.
- Develop scalable, reliable, and fault-tolerant Spark applications following industry best practices and coding standards.
- Optimize and tune Spark jobs and Elasticsearch queries to improve performance, scalability, and resource utilization.
- Monitor job performance, identify bottlenecks, troubleshoot issues, and implement appropriate optimizations.
- Troubleshoot and resolve issues related to data processing, data quality, Spark performance, and Elasticsearch integration.
- Ensure data quality, consistency, accuracy, and integrity throughout the data processing lifecycle.
- Design and deploy data engineering solutions on OpenShift Container Platform (OCP) using containerization and orchestration technologies.
- Optimize data engineering workflows for containerized environments and efficient resource utilization.
- Collaborate with DevOps teams to streamline deployments and implement CI/CD pipelines.
- Implement data governance, data lineage, and metadata management practices to ensure data accuracy, traceability, and compliance.
- Implement monitoring and logging mechanisms to ensure the health, availability, and performance of data infrastructure.
- Monitor and optimize end-to-end data pipeline performance and implement required enhancements.
- Document data engineering processes, workflows, architecture, and infrastructure configurations for knowledge sharing and future reference.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology, or a related field.
- Quantexa Certified Data Engineer / Data Architect with hands-on experience and strong proficiency in the Quantexa platform.
- Proven experience as a Data Engineer working with Hadoop, Spark, and large-scale data processing technologies.
- Strong proficiency in Scala and familiarity with functional programming concepts.
- In-depth understanding of Apache Spark architecture, RDDs, DataFrames, Datasets, and Spark SQL.
- Strong expertise in Hadoop ecosystem technologies, including HDFS, Hive, Pig, and related tools.
- Hands-on experience with Elasticsearch, including data indexing, search applications, data modeling, indexing strategies, and query optimization.
- Experience with OpenShift Container Platform (OCP) and Kubernetes-based container orchestration.
- Strong programming skills in Scala, Python, Java, and/or Spark.
- Good understanding of DevOps practices, CI/CD pipelines, and infrastructure automation.
- Experience with tools such as Docker, Jenkins, Ansible, and Bitbucket.
- Experience with distributed computing, parallel processing, and large-scale datasets.
- Strong experience in performance tuning and optimization of Spark applications and Elasticsearch queries.
- Experience with Git and collaborative software development workflows.
- Strong analytical and problem-solving skills with the ability to troubleshoot complex technical issues.
- Excellent communication and collaboration skills with the ability to work effectively with cross-functional teams.
- Experience with Grafana, Prometheus, and Splunk will be an added advantage.
- Exposure to cloud platforms such as AWS, Azure, or GCP and their data services will be a plus.
- Knowledge or experience in the Compliance / AML domain will be an added advantage.