• Develop software solutions supporting quantitative analysis and modeling for credit ratings data • Analyze NRSRO data disclosures for compliance with applicable rules and statutes • Maintain analytical frameworks for utilizing data and quantitative analyses to identify potential areas for further inquiry • Conduct data-driven research related to the credit rating industry; summarize findings concisely through charts and graphs • Test deployment and functionality of internal systems and applications after new features are added • Document and quality-assure all project work prior to client delivery • Attend meetings with agency staff, securities industry participants, other government agencies, and relevant external organizations as needed • Contribute meaningfully to building Peregrine as an enterprise — whether through leading business development pursuits, developing internal tools or intellectual property, mentoring colleagues, or maintaining an active publication record • Collaborate with firm leadership to identify and drive initiatives that strengthen Peregrine's capabilities and market position

• Collaborate with data engineering, analytics, product, and marketing teams to design and implement reliable, scalable data models and transformation pipelines. • Develop and maintain dbt models, macros, and documentation that ensure data accuracy, reusability, and clarity across the organization. • Partner with business stakeholders to translate analytical needs into well-structured datasets that support reporting and self-service analytics. • Support and uphold best practices for data modeling, testing, version control, and documentation across analytics workflows. • Proactively identify and address issues in data quality, model performance, and pipeline efficiency. • Contribute to the standardization of metrics, definitions, and semantic layers to ensure consistent reporting across business units. • Participate in code reviews and knowledge-sharing to continuously improve team processes and data craftsmanship. • Stay current with modern data tools, frameworks, and best practices to help evolve our analytics engineering stack.

• Establish the foundation of LiveKit's analytics practice • Translate business concepts into robust data models • Create actionable KPIs • Enable data-driven decision making across the organization • Use AI coding assistants extensively and evolve AI development workflows • Resist the urge to over-engineer • Spend significant time with stakeholders to memorialize business logic and create trusted metrics • Ensure everything built needs clear documentation for users and AI systems • Follow engineering best practices with PR reviews and automated testing • Comfort with ambiguity and rapid iteration

• Apply hands-on analytics engineering expertise to solve complex, fast-paced business and clinical problems using data. • Design, develop and maintain scalable, analytics ready data models and pipelines (primarily in dbt) that power clinical program reporting, metrics and insights in our Unified Data Warehouse (UDW). • Partner closely with clinical program leaders, analysts, product teams, and operations stakeholders to understand program workflows (e.g., enrollment, adherence, outcomes, follow ups) and translate them into intuitive, well-documented data models. • Build curated data marts and semantic layers that make analytics for clinical programs (such as diabetes and weight management) easy to self-serve for dashboards, ad-hoc analysis and operational reporting. • Own KPI and metric definitions end-to-end, from raw source data through production-grade models, ensuring consistency, transparency and trust in reporting. • Implement automated data validation, testing and quality checks to ensure high data reliability across clinical and operational datasets. • Continuously optimize data models and warehouse performance for both cost and speed as usage scales. • Contribute to analytics governance by establishing guardrails, documentation and best practices that improve accountability, ownership, and data literacy across teams. • Communicate insights effectively by developing clear data memos, documentation and presentations tailored to clinical and business stakeholders. • Explore and integrate AI-enabled analytics and predictive modeling use cases (e.g., adherence risk, program outcomes, operational forecasting) into the analytics stack. Experience operationalizing ML models is a plus.