Sr Big Data Engineer - Oozie and Pig (GCP)

As a Senior Big Data Engineer at Rackspace Technology, your main responsibilities include designing and developing scalable batch processing systems, writing production-ready code, managing complex data workflows within the Apache Hadoop ecosystem, and leveraging Google Cloud Platform tools to build big data solutions. You will also be involved in implementing DevOps practices and collaborating with cross-functional teams to ensure operational excellence.

To qualify for the Senior Big Data Engineer role at Rackspace Technology, candidates should possess a Bachelor's degree in Computer Science or a related field, with at least 5 years of relevant experience in building and operating solutions on cloud platforms, particularly GCP. Proficiency in technologies like Oozie, Pig, Java, and Python, along with a strong understanding of the Apache Hadoop ecosystem and infrastructure as code (IaC) practices, is essential.

For the Senior Big Data Engineer position at Rackspace Technology, proficiency in Java, especially with Spark, and Python is crucial. Familiarity with Pig and SQL is also essential, as these languages play a key role in developing data solutions and workflows within the Hadoop framework.

Experience with public cloud services is highly critical for the Senior Big Data Engineer role at Rackspace Technology. Knowledge of Google Cloud Platform (GCP) and managed cloud services, along with an understanding of cloud-based batch processing systems, will significantly aid in the design and implementation of scalable and efficient data solutions.

As a Senior Big Data Engineer at Rackspace Technology, you can expect a remote-first work environment that promotes independence, collaboration, and a commitment to excellence. You’ll work closely with cross-functional teams, contributing to the company’s mission to deliver innovative technology solutions while fostering a dynamic and supportive culture.

Certainly! I have utilized Oozie as a workflow scheduler in my previous projects to manage complex data processing jobs. Oozie's ability to orchestrate Hadoop jobs made it easy to define dependencies and ensure tasks were executed in the correct order. I can provide specific examples of workflows I designed that efficiently handled batch jobs, ensuring timely data availability.

Optimizing performance in batch processing systems involves a multi-faceted approach. I typically analyze data flow patterns, identify bottlenecks in processing, and utilize efficient data structures. I also leverage parallel processing capabilities of Hadoop and optimize queries in Pig and Hive to reduce execution time. Continuous testing and monitoring play key roles in ensuring performance improvements.

In a previous project, I was tasked with migrating an on-premises data processing system to GCP. The challenge lay in handling data volume and compatibility. I tackled this by incrementally migrating datasets, thoroughly testing integration with GCP services like Dataproc and ensuring minimal downtime during the transition. Communication with stakeholders was also crucial throughout the process.

I have implemented Infrastructure as Code using tools like Terraform to automate provisioning and management of cloud resources. In terms of CI/CD, I developed pipelines that enable seamless code delivery and integration for data workflows. By automating testing and deployment, I ensured that our data processing systems were consistently high-quality and reliable.

To ensure data integrity and reliability, I implement checks at various stages of the data processing workflow. This includes validations, error logging, and utilizing monitoring tools to receive alerts on any discrepancies. My focus is on building resilient systems that can gracefully handle failures and allow for easy recovery and rerun of processes when needed.

Effective communication is key when working with cross-functional teams. I prioritize clarity by breaking down technical concepts into easily understandable segments for stakeholders from non-technical backgrounds. Regular check-ins and collaborative tools also facilitate openness and feedback. I find using visual aids, like diagrams, can help bridge gaps in understanding.

My skills in both Java and Python are instrumental in my role as a Senior Big Data Engineer. With Java, I leverage its robust libraries and performance efficiency for large-scale applications, while Python allows for rapid development and prototyping, especially when dealing with data manipulation and analysis tasks. Each language aids in addressing different aspects of data engineering challenges.

I have extensive experience with the Apache Hadoop ecosystem, utilizing components like HDFS for storage, MapReduce for batch processing, and HBase for real-time data access. I regularly work with tools like Pig and Hive to write queries and manage data efficiently. This experience has provided me with insights into designing scalable data architectures that effectively handle massive datasets.

Best practices for developing batch processing solutions include designing for scalability, ensuring clear documentation, and maintaining strong monitoring and logging. I also emphasize modular coding practices, which facilitate easier debugging and testing. Regularly reviewing performance metrics and incorporating feedback helps refine the solutions to align with user needs.

The big data landscape is continuously evolving with new tools and methodologies emerging regularly. I stay updated by engaging in online courses, participating in tech meetups, and following industry blogs and forums. Regular hands-on practice with emerging technologies and experimenting with new solutions is also key to maintaining a competitive edge in this dynamic field.