• Design, develop, and maintain ETL data pipelines supporting daily data transfers between the core application and the Enhanced Reporting Capability (ERC) module. • Perform secure data exchanges using SFTP between internal and external interfacing systems, supporting up to 300 data exchanges annually • Develop, maintain, and automate system interfaces and data transfer processes, including implementation of modern API-based data exchanges • Build reusable processes to connect to multiple data sources across cloud environments and integrate them into the enterprise architecture • Provide data management and governance services, including data ingestion, validation, storage, management, and quality assurance in accordance with Government-approved standards • Create data flow diagrams, perform data standardization, and support enterprise data modeling activities • Manage incoming data streams and develop mechanisms that allow users and applications to modify, access, and extract data efficiently • Identify, analyze, and resolve data quality issues, and design and implement automated data quality monitoring and control mechanisms • Conduct ongoing data quality analysis, documentation, and reporting to assess overall data health and integrity • Develop and maintain data quality dashboards and reports, including system metrics and validation checks between system data and interfacing Service systems • Automate data anomaly detection and monitoring to proactively identify inconsistencies or errors • Support database performance optimization, data recovery, and system integrity, including configuration and maintenance of database instances • Modify database structures to support application modernization efforts, including database migrations, conversions, and schema updates • Manage the migration of program updates, database changes, reference data, and configuration changes across the development lifecycle • Generate transaction analysis reports for external interfaces, including data exchange volumes, error rates, and issue tracking • Support enhanced reporting capabilities that enable congressionally mandated reporting, trend analysis, data validation, and ad hoc reporting requests • Develop business intelligence (BI) reports and dashboards as requested by the DHRA Data Governance Board and DMDC Data Stewardship Council • Integrate data from additional DoD environments such as Advana and Oracle Cloud Infrastructure (OCI) to support enterprise reporting initiatives

Senior Analytics Engineer (JACKSONVILLE, FL) ARC Group has an immediate opportunity for a Senior Analytics Engineer ! This is starting out as a 12 month contract position with potential to extend longer or possibly convert to FTE. This is a fantastic opportunity to join a dynamic and well-respected organization offering tremendous career growth potential. 100 Percent Remote Senior Analytics Engineer (SAS to Python Modernization) Position Overview We are seeking a Senior Analytics Engineer to support the modernization of legacy SAS-based analytics and reporting workflows within a Business Informatics organization. The role focuses on translating existing SAS programs into scalable, maintainable Python-based analytics solutions while preserving business logic and data accuracy. This is not a traditional software engineering role and not a pure reporting position. The ideal candidate combines strong analytical judgment, hands-on Python data expertise, and the ability to interpret and refactor complex SAS logic into modern analytics pipelines. Key Responsibilities - Analyze and interpret existing SAS programs including DATA steps, PROC SQL, and analytical procedures - Translate SAS-based transformations and analytics into Python using pandas, numpy, and SQL while preserving business logic - Validate converted outputs by reconciling row counts, aggregations, joins, and derived metrics between SAS and Python - Partner with Business Informatics stakeholders to understand reporting intent, metric definitions, and downstream usage - Refactor and simplify legacy logic to improve transparency and reduce technical debt - Collaborate with data platform and technology teams to align Python solutions with enterprise standards - Document data transformations, assumptions, and business rules to ensure long-term maintainability Required Qualifications - Strong experience in Python for data analysis (pandas, numpy, SQL integration) - Working knowledge of SAS with the ability to read and interpret existing SAS code - Experience modernizing or translating legacy analytics/reporting logic - Solid understanding of analytics concepts including joins, aggregations, time-based logic, and derived metrics - Experience validating analytical outputs and ensuring data accuracy - Ability to translate business analytical requirements into technical implementations Preferred Qualifications - Experience in healthcare, payer, or regulated data environments - Familiarity with SAS macros and common PROC workflows - Exposure to modern data platforms or cloud-based analytics environments - Background in analytics engineering, business intelligence, or data transformation roles Key Skills - Python (pandas, numpy, SQL) - SAS (DATA step, PROC SQL, Macros) - Legacy analytics modernization - Data validation and reconciliation - Business stakeholder collaboration - Healthcare analytics (preferred)

• Join a team evolving how analytics is done across the business. • Collaborate closely with Data engineers, Data analysts, ML specialists, and stakeholders across both business and product domains. • Build reusable, well-structured data models for analytics. • Support decision-making with a consistent foundation for business concepts.

• Design, build, and maintain scalable data pipelines that ingest and transform data across Salesforce, Gainsight, Rocketlane, Zendesk, Snowflake, product telemetry, and other core systems • Strategic partner with Customer Ops & Analytics, Enterprise Data Engineering and Product Operations teams to be a thought leader to design scalable data solutions. • Develop reliable data models that power renewal forecasting, churn prediction, customer health scoring, professional services metrics, support performance, and product usage • Implement CI/CD, data testing, and documentation best practices to ensure scalable, secure, and auditable transformations • Optimize warehouse performance and data architecture for speed, cost efficiency, and reliability • Partner with Product and Engineering to design telemetry schemas and ensure meaningful product instrumentation, while building pipelines that translate product usage data into actionable insights for both Product teams and Post-Sales teams • Architect data structures that support AI/ML use cases including churn prediction, renewal forecasting, workflow automation, and sentiment analysis • Implement monitoring and validation frameworks to proactively detect data inconsistencies • Familiarity with using Python for basic to advanced data processing tasks • Develop and document data or system models, flow diagrams and architecture guidelines