Overview At Cotiviti, we are custodians of data for our clients. Using their technical experience in ETL processes, Data Engineers ensure operational functions are occurring as expected. This includes but is not limited to managing data implementations, data integrations, data production, data quality, and data security. Responsibilities - Create, maintain and execute intermediate to advanced Spark scripts for data management and data validation, and data integration. - Create, maintain and execute basic to intermediate SQL scripts for data management and data validation. - Optimize the queries to improve the efficiency of daily tasks. - Perform data analysis and identify any issues. - Work with other groups such as Engineering team, DBA, Cloud ops, etc. to troubleshoot and resolve any environmental or network issues that impact your work. Extend your support to after – hours or weekends as needed. - Create and maintain data pipelines as needed. - Validates the tasks results to ensure that all the requirements are met. - Adhere to all the industry level and organization level compliance rules and regulations to maintain data integrity. - Complete individual productivity tracking. - Complete task assignments using department ticketing system within assigned deadline. - Achieve organizational and individual goals as identified in performance reviews and goal setting exercises. - Complete all special projects and other duties as assigned. - Must be able to perform duties with or without reasonable accommodation. This job description is intended to describe the general nature and level of work being performed and is not to be construed as an exhaustive list of responsibilities, duties and skills required. This job description does not constitute an employment agreement and is subject to change as the needs of Cotiviti and requirements of the job change. Qualifications - Bachelor’s degree in Computer Science, Information Technology or equivalent work experience. - 3+ years of working knowledge of big data technologies (Spark, S3, Kafka, Ray, Hadoop, etc.). - 2+ years of working knowledge of big data / cloud technologies (Databricks, AWS, Azure, Hadoop, Spark, Snowflake etc.). - 3+ years of working knowledge of cloud (AWS, Azure, GCP, OCI etc.). - 3+ years of working knowledge of RDBMS (Oracle, MS SQL, Vertica, etc.) and experience using SQL, PL/SQL or other data integration/ETL tools. - Any Databricks / AWS certifications is a big plus. - Familiarity with data pipeline orchestration tools (e.g., Airflow, Databricks Workflows). - 3+ years of data analysis. Preferably in the Healthcare industry of enrollment, medical claims and/or pharmacy claims. - Proficient in Microsoft Office Suite applications PowerPoint, Word, Excel and Outlook. - Flexible work schedule. - Experience with project management tools like JIRA. - Databricks and/or Snowflake environment familiarity a plus. Mental Requirements: - Strong analytical skills. - Excellent verbal, listening and written communication skills. - Ability to multitask and prioritize projects to meet scheduled deadlines and tight turnaround times. - Ability to work well independently or in a team environment. Working Conditions and Physical Requirements: - Remaining in a stationary position, often standing or sitting for prolonged periods. - Repeating motions that may include the wrists, hands, and/or fingers. - Must be able to provide a dedicated, secure work area. - Must be able to provide high-speed internet access / connectivity and office setup and maintenance. Base compensation ranges from $105,000 to $125,000 per year. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs. Cotiviti offers team members a competitive benefits package to address a wide range of personal and family needs, including medical, dental, vision, disability, and life insurance coverage, 401(k) savings plans, paid family leave, 9 paid holidays per year, and 17-27 days of Paid Time Off (PTO) per year, depending on specific level and length of service with Cotiviti. For information about our benefits package, please refer to our Careers page. Since this job will be based remotely, all interviews will be conducted virtually. Date of posting: Applications are assessed on a rolling basis. We anticipate that the application window will close on 07//2026, but the application window may change depending on the volume of applications received or close immediately if a qualified candidate is selected. #LI-REMOTE #LI-LL1 #SENIOR

• Secure data sources, design data models, and build distributed components, pipelines, and tools that enable the organization to make analytical, data-driven decisions. • Develop and automate large scale, high-performance data processing systems (batch and streaming). • Build scalable data pipelines leveraging orchestration technologies. • Design our data models for optimal storage and retrieval to meet critical product and business requirements. • Understand and influence logging to support our data flow, architecting logging best practices where needed. • Contribute to shared Data Engineering tooling & standards to improve productivity and quality of output for Data Engineers across the company. • Improve data quality by using & improving internal tools to automatically detect issues. • Design and implement data pipelines and data summarization processes using SQL and apply understanding of relational data structures. • Create a summary data platform supporting our presentation layer that allows clinicians and operators to pinpoint interventions on-demand to patients most in need. • Locate trends in data sets and build algorithms for large and complex data sets. • Analyze and design the best ways to expand our data model to incorporate mission critical data. • Analyze large data sets to identify gaps and inconsistencies, provide data insights, and advance effective product solutions. • Own projects to design and implement best-in-class data processing enabling clean data flow directly to employer’s data model and on to our presentation layer. • Assist with development of systems, strategies, and standards to ensure the quality of data collected, organize and maintain the data properly, and effectively deliver data needed by primary care physicians to manage patient healthcare. • Work with analytics, engineering, and operations to design and implement new analytics products that support improving patient health. • Follow data best practices, including engagement in project status meetings and self-education on new technologies and practices. • Design a new concept within our data model to meet a new operational or analytical need.

• Develop and automate large scale, high-performance data processing systems (batch and/or streaming) to drive business growth • Build scalable data pipelines leveraging orchestration technologies allowing for easy monitoring and troubleshooting • Design data models for optimal storage and retrieval • Improve data quality by using and improving internal tools to automatically detect issues • Create a summary data platform supporting presentation layer • Work with analytics, engineering, and operations to design and implement new analytics products

• Lead and mentor a team of data engineers, ensuring high performance, strong collaboration, and continuous skill development. • Define and drive the data engineering roadmap aligned with enterprise analytics strategy. • Partner with Finance, BI, and business stakeholders to understand requirements and translate them into scalable data solutions. • Oversee the design, build, and maintenance of data pipelines, data models, and ETL/ELT processes across Snowflake and Microsoft Fabric. • Architect and optimize Snowflake environments, including warehouse sizing, performance tuning, security, and cost management. • Lead the development of robust semantic models and datasets to support Power BI reporting and self-service. • Ensure data quality, governance, and documentation standards are consistently applied. • Build and maintain enterprise-grade data models (star/snowflake schemas, fact/dimension structures). • Support Finance with data structures that enable forecasting, GL reporting, profitability analysis, and other financial analytics. • Collaborate with BI developers to ensure models are optimized for Power BI performance and usability. • Implement best practices for CI/CD, version control, testing, and deployment of data pipelines and models. • Monitor and troubleshoot data refreshes, pipeline failures, and performance issues. • Drive automation and efficiency improvements across the data engineering lifecycle.