Enterprise Analytics – Data Engineer

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Enterprise Analytics – Data Engineer

Company Overview

A leading institution in data analytics and technology solutions, this organization leverages cutting-edge tools to deliver scalable, reliable, and efficient data engineering processes. Its commitment to continuous improvement and innovation empowers businesses to harness the power of big data and machine learning technologies.

Role Overview

The Data Engineer will design, develop, and maintain robust data pipelines to process large-scale datasets. This role requires expertise in Hadoop, Spark, OpenShift Container Platform (OCP), and DevOps practices to ensure platform scalability, reliability, and efficiency for machine learning solutions.

Key Responsibilities

• Implement data transformation, aggregation, and enrichment processes for data analytics and machine learning.

• Collaborate with cross-functional teams to gather data requirements and deliver effective engineering solutions.

• Maintain data quality, accuracy, and compliance through governance practices and metadata management.

• Design and deploy containerized data engineering solutions on OpenShift Container Platform (OCP).

• Optimize workflows for containerized environments and implement CI/CD pipelines in collaboration with DevOps teams.

• Monitor and troubleshoot data pipeline performance while implementing enhancements.

• Stay updated on emerging technologies and best practices in data engineering and DevOps.

• Document workflows, configurations, and processes for future reference and team knowledge sharing.

• Provide mentorship and technical leadership to junior team members.

Requirements

• Bachelor’s degree in Computer Science, Information Technology, or a related field.

• Proven experience with Hadoop, Spark, and large-scale data processing technologies.

• Expertise in data infrastructure tools such as HDFS, Hive, and Pig.

• Hands-on experience with containerization platforms (e.g., OpenShift, Kubernetes).

• Proficiency in programming languages such as Python, Scala, or Java.

• Knowledge of DevOps practices, CI/CD tools (e.g., Docker, Jenkins, Ansible), and infrastructure automation.

• Experience with monitoring tools (e.g., Grafana, Prometheus, Splunk) is an advantage.

• Strong problem-solving skills and a proactive approach to technical challenges.

• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and their data services is a plus.

• Excellent collaboration and communication skills to work with cross-functional teams.

Disclaimer:

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