Staff Software Developer

IQVIA•Wayne, PA

About The Position

The Staff Software Engineer will play a critical role in the onboarding, transformation, integration, and provisioning of large-scale patient-level healthcare datasets into core data assets that power the IQVIA Scion Platform. This role requires a strong combination of software engineering, data engineering, cloud technologies, and healthcare domain expertise to deliver scalable, high-quality data products that support IQVIA's commercial and analytical offerings. The ideal candidate will work closely with internal stakeholders and external data suppliers to gain a deep understanding of healthcare data assets, business requirements, and data models. Leveraging this knowledge, the candidate will design, develop, and optimize data-driven solutions that transform raw healthcare data into production-ready products that can be utilized by IQVIA clients and internal analytical teams. This position will have growth opportunity to help transform data and create applications on our next generation patient platform.

Requirements

5-10 years of experience with large scale data integration and data movement activities.
Experience Lead architecture and coordination with cross functional and offshore teams to lead development.
The candidate be able to function as development lead in scrum and lead the team to finish sprint/PI objectives.
Cloud architecture and hands-on development: Snowflake (needed)
Hadoop with programming experience in: Spark Scala Python SQL Linux shell scripting
Building code with AI tools, especially Cursor.
Review Performance tuning experience.

Nice To Haves

Databricks/Redshift (a plus)
Experience working with LLM and building functionality with AI agents (Preferred).
Experience working with healthcare, pharmacy, claims, clinical trial, Electronic Medical Record (EMR), or longitudinal patient data.
Experience in healthcare analytics environments preferred.
Experience in claims processing and claims engine platforms or Real-World Data (RWD) platforms or healthcare research data platforms.
Experience with healthcare data integration and Patient-Level Data warehousing.

Responsibilities

Collaborate directly with external data suppliers and internal stakeholders to understand data content, business rules, data models, and source system characteristics.
Analyze and integrate complex healthcare datasets, including claims, pharmacy, EMR, and longitudinal patient-level data, into enterprise data assets.
Design, develop, and maintain scalable and reusable data pipelines that ingest, transform, validate, and provision healthcare data from internal and external cloud-based sources.
Develop large-scale data processing solutions utilizing Snowflake on Azure as the primary enterprise data platform, while supporting legacy Hadoop environments leveraging Apache Spark and Scala.
Design and implement robust ETL/ELT frameworks and data movement solutions capable of processing high-volume healthcare datasets efficiently and reliably.
Partner with cross-functional engineering, architecture, data management, and DevOps teams to design solutions and resolve complex data integration and pipeline challenges.
Develop automated data quality frameworks, validation procedures, trend analysis reporting, and monitoring solutions to ensure data accuracy, completeness, consistency, and reliability.
Build clean, maintainable, and reusable code using software engineering best practices, emphasizing scalability, performance, and operational supportability.
Utilize Git source control and collaborate with DevOps teams to implement CI/CD pipelines, automated testing, deployment automation, and release management processes.
Participate actively in Agile development processes, including sprint planning, daily stand-ups, backlog refinement, retrospectives, and other Scrum ceremonies.
Contribute to technical design reviews, architecture discussions, peer code reviews, and engineering best practice initiatives.
Leverage AI-assisted development tools, including Cursor and similar technologies, to improve software development productivity, code quality, quality assurance processes, and data analysis activities.