Hadoop Admin Ops/SRE

As a Hadoop Admin Ops/SRE, your main responsibilities will include managing and optimizing Hadoop clusters, ensuring smooth platform operations, and integrating solutions across various applications. You will work with a range of data technologies including Hadoop, Spark, and Kafka, while collaborating with clients and vendors to provide effective technical solutions.

For the Hadoop Admin Ops/SRE position, essential skills include a strong understanding of Hadoop architecture, experience with cluster management, technical proficiency in Unix/Linux, and programming knowledge in Java or Python. Familiarity with tools like Docker, Ansible, and monitoring systems is also crucial for managing platform operations.

In the Hadoop Admin Ops/SRE role, you will work closely with agile development teams, project managers, and client partners. Effective communication is key, as you'll be liaising between technical and non-technical stakeholders to ensure user needs are met through tailored solutions.

A Hadoop Admin Ops/SRE can expect to work on a variety of complex and highly visible projects that leverage Hadoop and Data Science platforms. This includes upgrades, incident management, and deploying solutions tailored to optimize data operations and ensure high availability.

As a Hadoop Admin Ops/SRE, there are numerous growth opportunities available. You will have access to advanced training in Big Data technologies, the chance to lead projects, and the ability to specialize in specific areas like cloud solutions or AI/ML integration, promoting both personal and career development.

In answering this question, emphasize your practical experience in managing Hadoop clusters, discussing specific tools you've used like Cloudera or Hortonworks, and share examples of how you've optimized cluster performance in past roles.

Discuss your approach to monitoring and alerting by mentioning specific tools and practices, such as using YARN for resource management and integrating logging systems to ensure optimal performance and reliability.

Describe your methodical approach to bug fixes and deployments, focusing on processes like CI/CD with Jenkins, testing protocols, and any automation tools (like Ansible) you utilize to streamline these operations and ensure minimal downtime.

Share relevant programming languages you've worked with, including Java and Python, and discuss how you've used these skills in scripting for data pipeline automation, cluster management, or performance tuning in Hadoop contexts.

Provide a detailed example of a challenging technical issue, discussing how you approached the problem, the steps you took to troubleshoot and resolve it, and the ultimate outcome and what you learned from the experience.

Address your strategies for ensuring data security in Hadoop, such as implementing AD/Kerberos authentication, using encryption techniques, and maintaining compliance with industry standards to protect sensitive data.

Discuss your perspective on automation's importance in enhancing efficiency and reliability in operations, mentioning tools like Ansible and Jenkins you’ve employed to automate routine tasks, reducing errors and freeing up time for strategic initiatives.

Highlight any experience you have with technologies like Spark, Kafka, or additional Data Science tools, providing context on how you've integrated these technologies with Hadoop to enhance data processing or analytics capabilities.

Articulate your method for task prioritization, emphasizing your ability to triage incidents based on severity and impact, your use of monitoring metrics to stay proactive, and your communication with stakeholders to manage expectations accordingly.

Talk about the job scheduling tools you're familiar with, such as Apache Oozie or other scheduling systems, and explain how you've implemented these tools in previous roles to ensure timely and efficient job executions in Hadoop.