Lead Data Informaticist

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Lead Data Informaticist based in the United States. Overview The Lead Data Informaticist will play a pivotal role in building and governing the data infrastructure that powers advanced analytics and AI-assisted decision-making within a complex healthcare environment. You will design scalable data pipelines, curated analytical data products, and standardized business definitions that enable teams to access reliable, consistent, and actionable insights. Working extensively with modern cloud-based technologies, including Databricks, you will help establish trusted analytical environments that support self-service analytics and emerging AI capabilities. The role combines hands-on technical expertise with strategic problem-solving, ensuring that complex healthcare, pharmacy, financial, and operational data can be integrated and interpreted accurately. You will collaborate with analysts, data engineers, and business stakeholders to translate evolving analytical needs into reusable data solutions and governed business metrics. By strengthening data quality, metadata management, and analytical standards, you will improve the reliability and accessibility of insights across multiple business functions. This is an exciting opportunity for a seasoned data professional to influence analytics strategy, advance responsible AI adoption, and deliver measurable value through high-quality data products. Accountabilities • Design, develop, test, deploy, document, and maintain scalable ETL/ELT pipelines and curated analytical data products within Databricks and other modern data environments. • Translate business and analytical requirements into reusable data models that accurately represent pharmacy claims, membership, marketing, operational, financial, and other business domains. • Develop and maintain comprehensive metadata and documentation covering data sources, table and field definitions, analytical grain, relationships, lineage, refresh schedules, transformation logic, and intended usage. • Define, implement, and govern business rules, KPI calculations, population criteria, source relationships, and approved analytical methodologies to ensure consistent interpretation across teams. • Establish reliable methods for joining and integrating disparate datasets while preserving source integrity, business context, and analytical accuracy. • Build curated data environments that support governed self-service analytics and AI-assisted analytical tools, including Databricks Genie One. • Evaluate the accuracy, completeness, consistency, and reproducibility of AI-generated analytical responses, identifying gaps in existing data structures, business definitions, and governed capabilities. • Investigate recurring business questions that cannot be answered reliably using existing data assets and determine whether solutions require additional datasets, improved metadata, new business logic, or enhanced integrations. • Develop analytical connections across traditionally separate domains, enabling end-to-end analysis of pharmacy claims, membership, marketing activities, operational performance, and financial outcomes. • Implement automated data-quality controls, reconciliation processes, validation frameworks, and monitoring mechanisms to ensure trustworthy analytical outputs. • Partner with business-facing informaticists and analysts to understand emerging requirements and convert recurring analytical needs into scalable, reusable data capabilities. • Standardize important business definitions and analytical methodologies to minimize duplicated work and ensure consistency across reports, dashboards, and analytical applications. • Provide technical leadership through architecture recommendations, design standards, peer reviews, documentation practices, mentoring, and reusable development patterns. • Maintain source-to-target documentation and support change management, version control, testing, and deployment practices throughout the data development lifecycle. • Collaborate with data engineering, technology, finance, marketing, pharmacy, and other stakeholders to deliver integrated solutions that meet cross-functional analytical needs. • Evaluate emerging data technologies and AI capabilities, identifying responsible ways to improve analytical scalability, accessibility, governance, and business decision support. Requirements • Bachelor's degree in Computer Science, Data Engineering, Analytics, Informatics, Information Systems, Mathematics, Statistics, or a related technical or quantitative discipline, or equivalent relevant professional experience. • At least 7 years of experience in data analytics, informatics, business intelligence, data engineering, analytical engineering, or a related field, including relevant graduate or postgraduate experience. • Demonstrated experience leading complex analytical data initiatives, data product development, or analytical platform projects. • Hands-on experience working with modern cloud-based data and analytics environments. • Strong SQL skills, including experience developing complex analytical transformations, reusable queries, and reliable data assets. • Proven expertise in designing, developing, testing, and maintaining ETL/ELT pipelines. • Experience with dimensional, relational, or analytical data modeling and designing data structures that support downstream analytics and reporting. • Strong knowledge of data quality, reconciliation, testing, metadata management, data lineage, documentation, and analytical governance. • Experience working with healthcare, payer, pharmacy, claims, or other complex healthcare datasets. • Thorough understanding of data grain, source relationships, business rules, aggregations, population definitions, and the analytical implications of combining disparate datasets. • Ability to tra