• Design and own **business-critical metrics** (definitions, edge cases, documentation) • Build **clean, reusable SQL models** (transformation layer) that become source-of-truth datasets • Partner with stakeholders to turn ambiguous questions into clear analytical deliverables • Create self-serve datasets and lightweight reporting so teams can answer questions without bottlenecks • Improve data reliability: sanity checks, reconciliations, and data quality monitoring (as available) • Contribute to analytics best practices (naming, documentation, metric governance)

• Perform extensive SQL-based data transformations, integrating data from multiple internal and external sources (e.g., claims, media, CRM, EHR-derived datasets). • Define, validate, and govern metrics aligned to pharmaceutical business questions (e.g., brand performance, HCP engagement, patient journeys). • Apply statistical reasoning to analyze trends, identify drivers of performance, and ensure analytical rigor in insights and recommendations. • Maintain and iterate on analytical datasets and workflows as new data is ingested. • Conduct thorough exploratory data analysis (EDA) to uncover patterns, anomalies, and opportunities within complex healthcare datasets. • Interpret and translate analytical findings into clear business insights, tailored for stakeholders across marketing, analytics, strategy, and leadership teams. • Manually scrub, structure, and normalize data as needed to ensure data quality and analytical readiness, particularly for deep-dive analyses. • Prepare datasets to support advanced analytics, including exploratory and explanatory machine learning techniques, ensuring accuracy, consistency, and scalability. • Develop data visualizations and dashboards that clearly communicate insights and support decision-making. • Collaborate cross-functionally with data science, technology, media, strategy, and client-facing teams to align analyses with business needs. • Document methodologies, assumptions, and data limitations to support transparency and reproducibility.

• Design and maintain robust data transformations and mappings to convert raw medical claims into standardized canonical models. • Perform detailed data quality audits to detect, troubleshoot, and resolve issues impacting accuracy and completeness. • Write and optimize Spark SQL queries to validate data pipelines and support ongoing analytics. • Document data transformation logic and lineage to ensure transparency and reusability. • Collaborate cross-functionally with business stakeholders to translate data needs into technical specs. • Support onboarding of new client data feeds, ensuring smooth integration with minimal disruption. • Partner with data engineers to build and scale data pipelines, improving performance and reliability. • Act as a subject matter expert for healthcare claims data—advising on risk, trends, and business impact. • Work closely with data scientists to enable advanced analytics, ML models, and reporting use cases. • Troubleshoot ETL workflows and performance issues, driving continuous improvement in data architecture.

• Work with stakeholders to improve and implement air force acquisition data/analysis capability. • Develop and maintain dashboards and analytical applications using Qlik Sense. • Collect, clean, and validate supplier and company data from multiple sources. • Maintain and manage structured data sets to ensure data accuracy and integrity. • Develop and maintain SQL queries, stored procedures, and database objects. • Administer and manage a small Microsoft Azure hosted SQL Server environment. • Perform advanced data analysis using Microsoft Excel (pivot tables, Power Query, formulas, automation). • General Data manipulation, automation, and exploratory analysis. • Generate recurring and ad hoc reports to support operational and strategic decisions. • Identify data discrepancies and implement corrective actions. • Collaborate with stakeholders to define reporting requirements and deliver actionable insights. • Ensure compliance with USAF and DoD policies.