Senior OLAP Performance Engineer

Key Responsibilities 

Platform Performance & Optimization 

• Continuously improve and maintain the performance of the OLAP/cube analytics platform through hands-on query tuning, cube optimization, and dashboard performance improvements. 

• Design, prototype, refine, and optimize cubes and semantic-layer structures, including hierarchies, partitions, aggregates, caches, and precomputed measures. 

• Proactively analyze and resolve performance bottlenecks by evaluating query execution patterns, underlying data structures, and resource utilization. 

• Assess vendor roadmaps by evaluating new platform capabilities, testing pre‑release features, and advising on opportunities to enhance performance and scalability. 

• Serve as the primary troubleshooter for slow-running queries, dashboard inefficiencies, concurrency issues, and refresh delays. 

• Optimize how BI/reporting tools connect to and query the OLAP layer by improving semantic structures and query paths. 

• Ensure the OLAP platform scales effectively in a multi-tenant environment, preserving tenant isolation, predictable performance, and cost efficiency as both user volume and data size grow. 

Monitoring, Operations & Release Management 

• Implement and maintain monitoring for cube performance, including query execution patterns, refresh behavior, caching efficiency, and overall environment health. 

• Define baseline performance metrics to detect degradations, conducting release-over-release performance analysis for platform upgrades, feature enhancements, and cross-team validation efforts. 

• Document performance findings, tuning techniques, and troubleshooting approaches to support knowledge sharing and consistent problem-solving. 

Leadership & Problem Solving 

• Demonstrate strong ownership and a self-starter mindset in identifying improvement opportunities and driving solutions independently. 

• Guide teams and stakeholders in understanding performance tradeoffs, data modeling decisions, and optimization strategies. 

Required Qualifications 

• Extensive hands-on experience with OLAP/cube technologies such as Kyvos, SSAS, Essbase, Kylin, or similar platforms. 

• Expert-level proficiency in MDX with demonstrated ability to diagnose and optimize complex MDX queries. 

• Strong SQL skills and ability to analyze data structures, lineage, and transformations that impact analytical performance. 

• Deep experience in multidimensional OLAP cube and semantic-layer design, including hierarchies, partitions, aggregates, caching strategies, and measure calculations. 

• Proven ability to troubleshoot and tune large-scale analytical workloads, including cube refresh performance and concurrency issues. 

• Experience with distributed environments such as Spark, Databricks, Hive, or Presto, and columnar storage formats such as Parquet, or ORC. 

• Hands-on experience in cloud environments (Azure strongly preferred), including VM tuning, ADLS optimization, and observability tooling. 

• Strong understanding of ETL/ELT pipelines and ability to reshape or precompute data to achieve required performance. 

• Excellent communication skills with the ability to articulate technical decisions and performance tradeoffs. 

• Experience with healthcare data (claims, eligibility, clinical, or member-level analytics) is preferred but not required. 

Education 

Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field required. 

Master’s degree in a related technical or analytics discipline preferred. 

Travel 

Occasional travel may be required for team collaboration or project support.