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IQVIA’s Global Technology and Operations (GTO) group’s responsibility is expanding. To effectively navigate this increased scope we're looking for a Senior Big Data Application Engineer to join the team.

Position Overview:

Join our Data Office as a Senior Big Data Application Engineer, where you’ll be a key player in optimizing our Big Data Factory operations. Our fast-paced team manages over 40 petabytes of data across thousands of workloads across public and private Hadoop CDP platforms, Snowflake Data Cloud, and Databricks. Your role will be critical in driving optimizations with minimal supervision, ensuring our big data processes are as efficient and cost-effective as possible.

Key Responsibilities:

• Develop advanced Spark-based optimizations to ensure optimal performance and cost-efficiency of our on-premises and cloud data platform infrastructure.

• Utilize deep expertise in Scala, Python, SQL, and TEZ to drive significant cost savings and performance enhancements.

• Lead initiatives for automating data transfers and implementing data reusability and archival strategies, significantly reducing storage and processing costs.

• Develop and enforce best practices and standards for Spark application development and deployment, ensuring consistency and reliability across our big data ecosystem.

• Conduct thorough performance analysis of Spark applications, optimizing code and queries to achieve peak efficiency.

• Optimize the utilization of our BI platforms, tailoring data models and dashboards to the specific needs of big data processing and analysis.

• Create and deliver training materials focused on Spark optimization techniques and best practices for our business unit resources.

Qualifications:

• Bachelor’s degree in Computer Science, Engineering, or a related field; Master’s degree preferred; or equivalent work experience.

• At least 8 years of deep data engineering experience focusing on Spark and SQL application development.

• Demonstrated expertise in big data technologies, including Apache Spark, Scala, Python, SQL, and TEZ.

• Proven track record of driving significant cost savings and performance improvements in a big data environment.

• Experience optimizing Spark code, Impala queries, and Hive partitioning strategy for better scalability, reliability, and performance.

• Experience with Hive performance optimizations, such as using distributed cache for small datasets, data partitions, bucketing in Hive, and Map Side joins.

• Proficiency in Snowflake, Databricks, Oracle, and Cloudera for data management and optimization.

• Exceptional analytical and problem-solving abilities, capable of conducting detailed technical analysis to uncover optimization opportunities.

• Excellent communication skills, with the ability to lead cross-functional teams and articulate complex technical concepts to non-technical stakeholders.