Cotiviti enables healthcare organizations to deliver better care at lower cost through advanced technology and data analytics that improve the quality and sustainability of healthcare in the United States. Cotiviti’s solutions increase transparency and collaboration between payers and providers while empowering them to reduce medical and administrative costs, enable better health, improve claims payment efficiency, streamline operations, drive interoperability, and advance value-based care. Its customers serve the majority of U.S. healthcare consumers, providing coverage and care for over 300 million members and patients. Additionally, Cotiviti offers data management and recovery audit services to the retail sector to improve business outcomes.

Overview:

The Data & Insights Analyst is responsible for leading the design, architecture, and delivery of advanced analytics, reporting, and data-driven solutions that generate measurable business value.

This role operates as a strategic partner to business and technical stakeholders, translating complex data into actionable insights and scalable solutions. The Analyst plays a key role in shaping data strategy, advancing modern data platforms (including cloud and big data ecosystems), and embedding AI/ML capabilities into analytics workflows. This individual is expected to influence best practices, and drive innovation across the Analytics function. This role requires a balance of hands-on technical expertise, business acumen, and leadership within an Agile delivery environment.

Responsibilities:

Support the architecture, design, and development of scalable analytics and reporting solutions across enterprise data platforms.

Partner with business stakeholders to define analytical strategies, frame problems, and deliver insights that drive decision-making.

Design and implement end-to-end data pipelines and workflows using modern big data and cloud technologies.

Develop and optimize data models, dashboards, and self-service reporting solutions using tools such as Tableau, MicroStrategy, or similar platforms.

Leverage Databricks platform capabilities (Delta Lake, Spark, notebooks, workflows) to build and maintain scalable data and analytics solutions.

Integrate AI/ML models and advanced analytics into production workflows, including predictive modeling, anomaly detection, and automation of insights.

Drive automation and operationalization of analytics solutions, including CI/CD practices and data quality monitoring.

Provide technical leadership in code reviews, design reviews, and architecture decisions, ensuring adherence to best practices.

Mentor and guide junior analysts and engineers, contributing to team capability development.

Collaborate with cross-functional teams to ensure data governance, security, and compliance standards are met.

Lead or contribute to the Analytics Center of Excellence, promoting reusable frameworks, standards, and innovation.

Communicate complex analytical findings clearly to stakeholders at all levels, including executive audiences.

Deliver solutions using Agile methodologies and actively contribute to sprint planning, backlog refinement, and retrospectives.

Maintain documentation, data lineage, and knowledge repositories for all solutions.

Complete all responsibilities as outlined in the annual performance review and/or goal setting.

Complete all special projects and other duties as assigned.

Must be able to perform duties with or without reasonable accommodation.

Minimum Qualifications:

Bachelor’s degree in Business Analytics, Finance, Economics, Statistics, Mathematics, Data Science, Information Systems, or related quantitative/business field.

4+ years of experience in advanced analytics, data engineering, or data science roles.

Proven experience delivering actionable insights and business impact from complex datasets.

Experience working in modern data platforms, including cloud-based or distributed environments.

Experience in healthcare analytics, claims data, or payment integrity strongly preferred.

Technical Skills:

Advanced proficiency in SQL across multiple platforms (e.g., Oracle, PostgreSQL, MySQL).

Strong programming skills in Python, Spark (PySpark/Scala), or R.
Hands-on experience with Databricks (Delta Lake, Spark optimization, job orchestration, MLflow).

Experience building and deploying machine learning models and working with AI frameworks (e.g., scikit-learn, TensorFlow, or similar).

Familiarity with Generative AI concepts, including prompt engineering, embeddings, or LLM-powered analytics use cases.

Experience with data pipeline orchestration tools and workflow automation.

Strong experience with business intelligence tools (Tableau, MicroStrategy, Power BI, etc.).

Knowledge of data warehousing, data modeling, and lakehouse architectures.

Experience with big data technologies (Hadoop ecosystem, Spark, etc.).