Discuss your experience with big data technologies and distributed computing frameworks you have utilized.

Oh, big data technologies? Sure, I've dabbled in Hadoop and Spark during my last gig at a retail analytics firm. We were dealing with large sets of consumer data and needed frameworks that could handle that scale. My role mainly focused on writing PySpark scripts to process this data for marketing insights. It was pretty heavy-lifting stuff, but yeah, I got it done and learned a lot about distributed computing along the way. I also had a chance to use AWS for storage, which made it simpler to scale our data needs up and down.

In my recent role as a Senior Data Engineer at Innovatech, I leveraged a range of big data technologies to optimize our data processing pipelines. For instance, I was instrumental in a project where we migrated our legacy data systems to a modern Hadoop-based environment, which involved extensive use of Hive and Oozie for data warehousing and workflow management. My proficiency in Python and Java came in handy for designing robust ETL processes using Apache NiFi and Sqoop. Additionally, on AWS, I engineered a solution with EMR to scale our analytics capabilities and integrate seamlessly with our existing S3 data lakes, all while maintaining data compliance standards. The transition improved our data processing times by over 40% and significantly increased system reliability.

During my tenure at GlobalTech, I was deeply immersed in the world of big data, pioneering solutions that harnessed the power of distributed computing to handle our vast data streams. My primary focus was on evolving our data architecture from traditional databases to a state-of-the-art ecosystem centered around Apache Kafka for real-time event processing and Cassandra for scalable NoSQL storage. I devised complex data models to represent our transactional data efficiently and orchestrated ETL workflows using Apache Flink, ensuring seamless data movement from our operational databases into our analytical environment, which was built on Hadoop and leveraged Hive, Pig, and custom MapReduce jobs for various analytics tasks. My responsibilities also included deploying these solutions on AWS leveraging EC2, EMR, and RDS to ensure high availability and cost-effective scalability. This approach significantly reduced our time to insight for critical business operations. We achieved a 99.9% uptime and a 60% cost reduction in data processing. I led a talented team of engineers through this transformation, fostering an environment of innovation and technical excellence, driving continuous improvement and ensuring our compliance with GDPR and CCPA regulations.