Staff Engineer - Data Platform Team

About McKesson Compile

Established in 1833, McKesson is a US Fortune 10 global leader in healthcare supply chain management solutions, retail pharmacy, healthcare technology, community oncology, and specialty care. We partner with life sciences companies, manufacturers, providers, pharmacies, governments, and other healthcare organizations to help provide the right medicines, medical products, and healthcare services to the right patients at the right time, safely and cost effectively.

Based in Bangalore India, McKesson Compile’s data is a comprehensive, full linked system of record for the US Healthcare market, with intelligence on 2M+ healthcare professionals (HCPs) and over 800K facilities. Compile’s data includes high capture medical and pharmacy claims, closed capture Medicare claims (100%), along with best-in-class provider affiliations and customer master.

At McKesson we deliver careers with purpose and potential. Our focus on better health starts with creating an inclusive environment with strong values where you can build a fulfilling career. You can count on us to provide you with resources and opportunities to grow and be your best, while contributing to our pursuit of improving lives.

Role:
We are seeking a highly skilled and experienced Staff Engineer to join our Data Platform Team. As a Staff Engineer, you will play a critical role in designing, developing, and optimizing our data infrastructure. You will leverage your expertise in Spark, Airflow, Python, Snowflake, and Databricks to drive innovation and efficiency in our data processes. A strong understanding of cloud concepts, ideally with exposure to Microsoft Azure, will be crucial in this role.
 

Key Responsibilities:

• Drive, design and develop scalable data frameworks and infrastructure to support business requirements.

• Lead the architecture and implementation of data processing pipelines using technologies such as Spark and Airflow/Prefect.

• Collaborate with cross-functional teams to define data engineering standards and best practices.

• Optimize and manage data warehouse solutions, like Snowflake, ensuring performance, reliability, and security.

• Utilize Databricks to enhance data processing capabilities and improve overall system performance.

• Develop and maintain robust ETL processes to support data integration and analytics needs.

• Provide technical leadership and mentorship to junior engineers within the team.

• Stay updated with the latest industry trends and technologies, integrating them into the platform as appropriate.

• Troubleshoot complex data issues, providing efficient and effective resolutions.

• Collaborate with stakeholders to understand data requirements and deliver solutions that meet organizational goals.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

• Proven experience as a Staff Engineer or in a similar role within a data platform team.

• Expertise in Apache Spark, Apache Airflow, Python, Snowflake, and Databricks.

• Strong understanding of cloud concepts, with a preference for experience in Microsoft Azure.

• Demonstrated ability to design and develop data frameworks and infrastructure.

• Experience with data modeling, ETL processes, and data warehousing.

• Excellent problem-solving skills with the ability to troubleshoot complex data issues.

• Strong leadership and communication skills, with the ability to work collaboratively in a team environment.

• Proficiency in other programming languages and data technologies is a plus.

Preferred Qualifications:

• Very comfortable with reading code, documentation for open source tools/libraries.

• Knowledge of machine learning and data analytics tools.

• Experience in a fast-paced, agile development environment.

If you are a visionary data engineer with a passion for building robust data platforms and a strong background in the required technologies, we would love to hear from you. Apply now to join our team and help us drive data excellence within our organization!