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
· 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
· Stay updated with emerging technologies, industry trends, and best practices in data engineering and DevOps
· Provide technical leadership, mentorship, and guidance to junior team members to foster a culture of continuous learning and innovation to the continuous improvement of the analytics capabilities within the bank.
Requirements:
· Bachelor's degree in Computer Science, Information Technology, or a related field
· Proven experience as a Data Engineer, working with Hadoop, Spark, and data processing technologies in large-scale environments
· 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
· Strong problem-solving and troubleshooting skills with a proactive approach to resolving technical challenges
· Excellent collaboration and communication skills to work effectively with cross-functional teams
· Ability to manage multiple priorities, meet deadlines, and deliver high-quality results in a fast-paced environment
· Experience with cloud platforms (e.g., AWS, Azure, GCP) and their data services is a plus
