Key Skills:
Quantexa certification mandatory
Job Objectives
We seek individuals with highly developed conceptual, strategic, and analytical skills, capable of striking a balance between visionary thinking and practical solutions. The ability to comprehend, inspire, and mobilize others is crucial. A business-oriented mindset coupled with effective storytelling will drive your success. We are looking for self-starters ready to take on responsibilities with enthusiasm.
Key Responsibilities
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. You will be working with Quantexa platform.
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.
Key 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.
- Must be Quantexa certified data engineer / data architect and proficient with the tool.
- 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.
- 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.