Role Description The Data Verification Specialist is responsible for all bill review clerical functions, including mail, prepping and scanning medical bills, data entry, data verification and assisting with various phone and email tasks to support the bill review department. This is a remote role. Essential Functions & Responsibilities: - Validate incoming data to the Bill Review system - Identify and apply customer-specific rules and processes - Requires continual and consistent communication with supervisor regarding status of Data Verification queues and workload - Assist the bill review department with all clerical duties as assigned - Additional duties as assigned Qualifications - Ability to work on several concurrent tasks and prioritize workload with minimal direction - Ability to identify, analyze and solve problems - Basic computer proficiency, including familiarity with Microsoft Office applications - Strong interpersonal, time management, and organizational skills - Ability to work both independently and within a team environment Requirements - High school diploma or equivalent - Experience in a professional office environment preferred Benefits - Comprehensive benefits package for full-time regular employees - Medical (HDHP) w/Pharmacy - Dental - Vision - Long Term Disability - Health Savings Account - Flexible Spending Account Options - Life Insurance - Accident Insurance - Critical Illness Insurance - Pre-paid Legal Insurance - Parking and Transit FSA accounts - 401K - ROTH 401K - Paid time off Company Description CorVel, a certified Great Place to Work® Company, is a national provider of industry-leading risk management solutions for the workers’ compensation, auto, health and disability management industries. CorVel was founded in 1987 and has been publicly traded on the NASDAQ stock exchange since 1991. Our continual investment in human capital and technology enable us to deliver the most innovative and integrated solutions to our clients. We are a stable and growing company with a strong, supportive culture and plenty of career advancement opportunities. Over 4,000 people working across the United States embrace our core values of Accountability, Commitment, Excellence, Integrity and Teamwork (ACE-IT!).

• Architect a Self-Healing, Autonomous Data Fabric. • Replace legacy ETL with a "nervous system" where metadata is active, governance is computational, and data sharing is zero-copy.

• Design and build net new resilient batch and real-time data pipelines that power critical products and drive rapid business growth. • Design and build data pipelines facilitating the migration from existing disparate data models and data sources, into new data models and databases in production. • Champion data reliability and performance by implementing proactive monitoring and quality-first engineering practices. • Optimize data models to support fast, efficient decision-making across high-impact product and business use cases. • Partner closely with product, engineering, and data science teams to quickly translate evolving business needs into scalable, production-ready data solutions. • Shape the future of data engineering at scale, helping define the long-term roadmap and setting best practices as we grow. • Coach and level up teammates, fostering a culture of learning, ownership, and continuous improvement. • Demonstrate strong self-direction and thrive in environments with high ambiguity, making sound decisions even when clear solutions are not readily apparent.

• Design, build, and maintain the internal data warehouse and analytical data layer, consolidating data from across our product and operational systems into a single reliable source of truth. • Define and enforce data models, schemas, and data contracts so that downstream consumers — data analysts and business teams — can trust and self-serve the data they work with. • Build and maintain transformation pipelines that turn raw internal data into clean, structured analytical datasets ready for BI, reporting, and AI use. • Collaborate with Data Analysts to enable AI and machine learning use cases on top of internal data — building the datasets and infrastructure they need to train models and run analytical workflows. • Implement data quality monitoring, lineage tracking, and observability across the warehouse so issues are caught early and data reliability is maintained over time. • Work with stakeholders across engineering, product, and business teams to understand their data needs and translate them into scalable, well-documented data models. • Champion good data engineering practices across the team: CI/CD for data assets, testing, documentation, and reproducibility.