BI Analytics Engineer, IQVIA Digital (Remote)

About this role

IQVIA Digital powers exceptional brand experiences, delivering innovative solutions based on a customer-first, insights-driven, and integrated omnichannel vision. We provide authenticated, privacy-enhanced data and analytics, innovative fit-for-purpose healthcare technology, and the expertise to enable an effective and adaptable marketing model that drives better quality of care and patient outcomes. IQVIA is the leading global provider of data, advanced analytics, technology solutions and clinical research services for the life sciences industry.  Learn more at www.iqviadigital.com

The Data Management team is responsible for developing, governing, and operationalizing IQVIA Digital's industry-leading healthcare and consumer data assets, which serve as the foundation for our products, analytics, business intelligence, and AI-enabled solutions. Working closely with Product and Technology teams, the organization ensures the quality, integrity, security, and scalability of data across the ecosystem. Leveraging robust data management practices and a privacy-by-design foundation, the team transforms complex data into trusted assets and actionable insights that drive innovation, inform strategic decisions, and deliver measurable value for clients across the healthcare marketing landscape.

Job Overview • Designs and manages the foundational analytical data structures that enable scalable business intelligence, self-service analytics, and consistent reporting across the organization. Serves as a key partner between business and technical teams, ensuring data is modeled and governed in a way that is intuitive for consumers, aligned with business needs, and sustainable as analytical capabilities mature.

Essential Functions • Designs, develops, and maintains semantic data models, including facts, dimensions, hierarchies, relationships, and business metrics that support enterprise reporting and analytics, helping establish the foundational architecture, standards, and patterns that will support the organization's long-term analytical capabilities while bringing structure and scalability to evolving business requirements and datasets. • Partners with business intelligence developers, analysts, and stakeholders to understand analytical requirements and translate them into scalable, reusable data structures. • Defines and implements data modeling standards and best practices, including dimensional modeling, slowly changing dimensions, historical data management, and conformed dimensions. • Establishes and maintains business-friendly semantic layers that simplify access to complex healthcare and product datasets while preserving data accuracy and consistency. • Collaborates with software engineering, data platform, and business teams to ensure semantic models align with source system design, enterprise data architecture, and organizational reporting needs. • Partners with analysts and business stakeholders to operationalize approved metric definitions and business rules within scalable, reusable semantic models. • Evaluates and optimizes model performance, scalability, and usability to support both dashboard-based reporting and ad hoc self-service analysis. • Participates in data architecture discussions and provides guidance on data design decisions that impact reporting, analytics, and data consumption. • Identifies opportunities to improve data accessibility, model reusability, and overall analytical maturity across the organization. • Validates data quality and model integrity through testing, monitoring, and collaboration with engineering and analytics teams. • Supports governance efforts by establishing standards for data definitions, metric calculation, documentation, and semantic layer usage. • Serves as a technical advisor on analytics initiatives, helping teams understand the implications of data design decisions on long-term scalability and maintainability. • Identifies opportunities to extend analytical data models and curated datasets to support self-service analytics, AI-assisted data exploration, and other emerging data consumption patterns.

Qualifications • Bachelor's Degree in Computer Science, Information Systems, Data Analytics, Engineering, or a related field, or equivalent experience. • 5+ years of experience in analytics engineering, business intelligence engineering, data modeling, data architecture, or a related field. • Strong experience designing and implementing dimensional data models, including fact and dimension tables, star schemas, and semantic layers. • Demonstrated understanding of data warehouse concepts and best practices, including slowly changing dimensions, historical tracking, surrogate keys, and data lineage. • Experience developing and optimizing data models that support business intelligence and self-service analytics solutions. • Proficiency in SQL and experience working with large, complex datasets. • Experience developing analytical data models within modern cloud data platforms such as Snowflake, BigQuery, Redshift, Synapse, or Databricks • Experience optimizing analytical data structures for performance, scalability, and self-service consumption • Experience partnering with business stakeholders to translate analytical requirements into scalable technical solutions. • Strong understanding of data governance, metric standardization, and analytical best practices. • Experience collaborating with software engineering, data engineering, and analytics teams in a cross-functional environment. • Ability to balance technical design considerations with business usability and accessibility requirements. • Strong written and verbal communication skills, with the ability to explain technical concepts to both technical and non-technical audiences. • Experience with healthcare data, product data, or other complex domain-specific datasets preferred. • Experience supporting Power BI or Tableau semantic models, tabular models, or equivalent business intelligence techno

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