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. \n

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 translate business definitions, KPI requirements, and analytical methodologies into scalable, repeatable technical solutions. Demonstrated ability to troubleshoot complex data issues, identify root causes, and resolve problems across upstream and downstream data processes. Excellent written and verbal communication skills, with the ability to explain technical architecture, data limitations, and analytical implications to both technical and nontechnical stakeholders. Proven ability to provide technical leadership, influence standards, guide solution design, and collaborate effectively across teams. Strong analytical thinking, attention to detail, and a commitment to data integrity, continuous improvement, and responsible technology adoption. Preferred qualifications: Master's degree in Computer Science, Data Engineering, Analytics, Informatics, Information Systems, Data Science, Statistics, or a related discipline. Significant hands-on experience with Databricks and its associated data engineering and analytics ecosystem. Experience developing curated data products for AI-assisted analytics, natural-language querying, or governed self-service analytical environments. Experience configuring, implementing, or supporting Databricks Genie One or comparable AI-assisted analytics platforms. Proficiency in Python or another programming language used for data engineering, automation, testing, or advanced analytics. Direct experience working with pharmacy or prescription claims data, including knowledge of pharmacy business concepts and healthcare data structures. Experience in payer, pharmacy benefit management (PBM), mail-order pharmacy, Medicare, or related healthcare environments. Experience integrating pharmacy claims, membership, marketing, operational, and financial datasets to support cross-functional analysis. Familiarity with enterprise KPI governance, semantic layers, reusable measures, metadata frameworks, and standardized business rules. Experience with enterprise data catalogs, lineage tools, governance frameworks, access controls, and healthcare data privacy requirements. Knowledge of data development lifecycle practices, including version control, code reviews, automated testing, and controlled deployments. Experience designing analytical assets that serve data analysts, business analysts, and business users through governed self-service tools. Remote work requirements: Reliable internet connectivity with a minimum download speed of 25 Mbps and upload speed of 10 Mbps. Wired cable or DSL connections are recommended. A dedicated home workspace that minimizes interruptions and safeguards confidential and protected health information in accordance with applicable privacy requirements. Availability to work a full-time schedule of 40 hours per week and willingness to travel occasionally for meetings or training. Benefits

Competitive annual salary:

$117,600–$161,700, depending on geographic location, qualifications, experience, education, certifications, and relevant skills. Performance-based bonus:

Eligibility for additional incentive compensation based on individual and/or organizational performance. Remote work flexibility:

Nationwide remote work arrangement, with occasional travel for business meetings or training. Medical insurance:

Healthcare coverage to support employees' health and well-being. Dental and vision insurance:

Benefits to support preventive care and overall health. Retirement savings:

Access to a 401(k) retirement savings plan. Paid time off:

Leave benefits to support personal needs, rest, and work-life balance. Paid holidays:

Company holidays and personal holidays. Family support:

Paid parental and caregiver leave to help employees manage important family responsibilities. Disability coverage:

Short-term and long-term disability insurance. Life insurance:

Coverage to provide additional financial protection. Technical leadership opportunities:

Influence data architecture, analytical standards, and the development of scalable data products. Innovation and professional growth:

Work with modern data platforms, AI-assisted analytics, and emerging technologies in a complex healthcare environment. Cross-functional collaboration:

Partner with specialists across data engineering, finance, pharmacy, marketing, technology, and analytics. Meaningful business impact:

Improve the quality, accessibility, and reliability of insights that support operational performance and strategic decision-making. \n

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