Senior Data Engineer- Healthcare/Lifesciences

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.

Join the fight against cancer. 
 
Ontada is a leading oncology real-world data and evidence, clinical education and provider technology business dedicated to transforming the fight against cancer. Part of McKesson Corporation, we support science through our data, technology and channels, which accelerate innovation for life science companies, support the education of community oncology providers and advance patient care. Together with our partners, we improve the lives of cancer patients.
As a Senior Data Engineer at Ontada, you will have the opportunity to make significant contributions to current and future versions of the analytic platform that transforms our customers’ clinical, financial, and operational data into actionable information that enables population health and performance management.

Be part of the team that is poised to transform the fight against cancer. Backed by the strength of a Fortune 9 company, our entrepreneurial organization develops technologies used by the oncology community to deliver evidence-based, personalized care, as well as insights used by biopharma companies to accelerate drug development and support the entire treatment journey. Our work powers informed decision-making at every pivotal moment in oncology – from the treatment options presented to patients, to the operational considerations for oncology practices, to the design of clinical trials, to the commercial launch plans for new therapies.

Key Responsibilities

• Design, implement, and optimize scalable and efficient data pipelines to support various data-driven initiatives.

• Lead the development of data architectures and contribute to the strategic direction of Ontada’s data platform.

• Drive data integration projects, ensuring seamless and optimized data flow across systems.

• Establish and enforce best practices for data engineering, ensuring data quality, reliability, and performance.

• Champion data modernization efforts by leveraging cloud-native solutions and optimizing data processing workflows.

• Mentor junior engineers and provide technical leadership across projects.

• Communicate complex technical concepts effectively to both technical and non-technical stakeholders.

• Automate deployments and manage environment promotions using GitHub CI/CD with GitHub Actions .

• Evaluate and recommend new technologies to enhance the data ecosystem.

Minimum Requirement

Degree or equivalent and typically requires 7+ years of relevant experience.

Education

Bachelor’s degree in Computer Science, Information Technology, Data Science, or a related field—or equivalent experience.

Critical Skills

• Proven expertise in building and optimizing data pipelines and large-scale distributed processing systems.

• Deep experience with Azure Cloud services, including Data Factory, Batch Service, Azure Gen2 Storage, and Azure SQL Database.

• Experience with AWS services related to data engineering and analytics.

• Strong hands-on experience with Databricks for scalable data processing and analytics.

• Proficiency with Snowflake for cloud data warehousing and transformation.

• Strong hands-on experience with Apache Spark and Big Data technologies.

• Proficiency in Infrastructure as Code (IaC) using tools like Terraform and Bicep.

• Experience implementing and managing Azure RBAC for secure data access.

• Strong programming skills in SQL, Python, PySpark, or Scala for data transformation and automation.

• Solid understanding of data engineering principles, data architecture, and database management.

• Experience designing and supporting enterprise-grade data platforms, including ingestion, storage, processing, and access layers.

• Strong understanding of distributed computing and scalable data processing architectures.

• Experience supporting machine learning workflows, including feature engineering, model training pipelines, and model deployment.

• Familiarity with Generative AI (GenAI) concepts