Lead Data Engineer

As a Lead Data Engineer, you will play a leading role in designing, building, and optimizing our data infrastructure, ensuring that it supports the advanced analytics need of the bank. You will oversee a team of data engineers, working closely with data analysts, DevOps team, infrastructure engineers, and other stakeholders to deliver high-quality data solution.

Your main responsibilities will include:

• 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.
• Collaborate with data engineers, data scientists, and other stakeholders to understand requirements and translate them into technical specifications and solutions.
• 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.
• 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, Data Engineering, Information Technology, or a related field.
• At least 10 years of 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, Ranger, 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 Grafana, Prometheus, Splunk will be an added benefit
• Strong problem-solving and troubleshooting skills with a proactive approach to resolving technical challenges.
• 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.