Senior OLAP Performance Engineer

Merative

2d•Remote

About The Position

Join a team dedicated to supporting the crucial mission of improving health outcomes. At Merative, you can apply your skills – and grow new ones – with colleagues who have deep expertise in health and technology. Merative provides data, analytics and software for the health industry. Our clients include providers, health plans, employers, life sciences companies and governments around the world. With industry-leading products and focused innovation, we help customers improve decision-making and performance so that together, we drive real progress in health. Learn more at merative.com About Us Truven is a platform offering real-world data, healthcare analytics, and member engagement solutions to help improve health and financial outcomes. We offer a deep understanding of the healthcare industry and time-tested processes to ensure precise, accurate insights. Truven provides information, analytic tools, benchmarks, research, and services to a variety of healthcare organizations. Our mission is to enable smarter decisions, reduce inefficiencies, and improve patient outcomes across the healthcare ecosystem. Learn more at merative.com Role Overview The Senior OLAP Performance Engineer is a hands-on expert responsible for the performance and scalability of the organization’s enterprise OLAP and cube-based analytics platform. This role focuses on deep MDX tuning, query optimization, dashboard performance tuning, and evaluating new platform capabilities. The ideal candidate is a self-starter with strong problem‑solving skills and a passion for diagnosing complex analytical performance challenges.

Requirements

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

Nice To Haves

Experience with healthcare data (claims, eligibility, clinical, or member-level analytics) is preferred but not required.
Master’s degree in a related technical or analytics discipline preferred.

Responsibilities

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.
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.
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.