About The Company

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care.

What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you.

About The Role The Senior Data Specialist at McKesson plays a pivotal role in designing, developing, and optimizing cloud-native data solutions on the Azure Data platform, with a primary focus on Azure Databricks. This position is responsible for building enterprise-scale data pipelines, implementing modern lakehouse architectures, and enabling trusted, high-quality data products that support analytics, artificial intelligence, machine learning, and strategic business decision-making.

The ideal candidate will possess extensive hands-on experience with Databricks, PySpark, advanced SQL, Azure Data Factory (ADF), Azure Data Lake Storage (ADLS), Delta Lake, Delta Live Tables (DLT), and Change Data Capture (CDC) frameworks. They will be instrumental in designing and supporting robust Medallion Architecture patterns while ensuring data quality, security, performance, and governance across the organization. Working closely with architects, product teams, analytics stakeholders, and governance teams, the Senior Data Specialist will deliver scalable, reusable, and production-ready data engineering solutions within a highly regulated environment.

Qualifications The ideal candidate will have a strong technical background combined with business acumen, and meet the following qualifications:

- Expert-level experience designing, building, and supporting enterprise-grade data solutions on Azure Databricks.
- Proficiency in PySpark and advanced SQL for large-scale data processing and optimization.




- Extensive experience with Azure Data Factory, Azure Data Lake Storage Gen2, Delta Lake, Delta Live Tables, Change Data Capture, and Medallion Architecture.
- Deep understanding of Spark architecture, job optimization, partitioning strategies, and performance tuning.
- Proven ability to architect and support end-to-end ETL/ELT pipelines in cloud-native environments.
- Strong knowledge of data modeling, data warehousing, metadata management, and data governance best practices.
- Experience with CI/CD pipelines, source control, automated testing, and deployment processes.
- Excellent troubleshooting skills for complex pipeline failures, data quality issues, and performance bottlenecks.
- Preferred certifications include Azure Data Engineer, Databricks Certified Data Engineer, or similar credentials.

Educational background includes a Bachelor's degree in Computer Science, Engineering, Information Systems, or related fields. A Master’s degree is a plus. Candidates should have at least 7 years of experience in data engineering and enterprise data platform development, with a minimum of 3 years of hands-on Azure Databricks experience in production environments. Experience in healthcare, pharmaceutical, life sciences, or other regulated industries is highly desirable.

Responsibilities

- Design, develop, and optimize large-scale, cloud-native data pipelines using Azure Databricks, PySpark, and advanced SQL techniques.
- Create and maintain batch and near real-time data ingestion frameworks leveraging Azure Data Factory, Delta Lake, and CDC methodologies.
- Implement and support modern Lakehouse architectures utilizing Medallion Architecture (Bronze, Silver, Gold layers) for scalable data management.




- Design and deploy Delta Live Tables pipelines to enhance data reliability, maintainability, and quality.
- Build scalable, resilient ETL/ELT frameworks capable of processing high-volume enterprise data efficiently.
- Optimize Spark workloads through effective partitioning, job orchestration, and performance tuning strategies.
- Establish monitoring, alerting, troubleshooting, and performance tuning practices across data pipelines to ensure operational excellence.
- Develop and implement data quality validation frameworks, lineage tracking, and metadata management to support data governance.
- Automate testing and reconciliation processes to ensure data accuracy, completeness, and consistency, supporting compliance and security standards.
- Collaborate with architects and stakeholders to translate business requirements into technical solutions that are scalable and sustainable.
- Drive engineering best practices, conduct code reviews, promote CI/CD adoption, and develop reusable frameworks.
- Mentor junior engineers, fostering a culture of continuous learning and technical excellence.
- Stay abreast of emerging Azure and Databricks capabilities, recommending enhancements to improve platform performance and operational efficiency.

Perks McKesson offers a competitive total rewards package that includes comprehensive health benefits, retirement plans, paid time off, and wellness programs. We prioritize employee growth and development through ongoing training, professional certifications, and career advancement opportunities. Our flexible work arrangements support work-life balance, and we foster an inclusive environment that values diversity and innovation. Additional benefits include performance-based incentives, employee assistance programs, and resources to support your overall well-being.

Equal Opportunity

McKesson is an Equal Chance Employer. We are committed to creating an inclusive environment where all employees and applicants are treated with respect and fairness. We do not discriminate based on

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