Finance Data Engineer — GCP Cloud Transformation

McKesson Corporation; Richmond (VA); Hybrid; USD 130,000 - 140,000; Full time

Job summary

McKesson Corporation is seeking a Data Engineer for Finance Solutions on Google Cloud Platform (GCP) in Richmond, VA. The role focuses on migrating and modernizing Finance data platforms to GCP, building scalable data solutions, and supporting AI/ML initiatives within a cross-functional Finance Data & BI team.

You will design, implement, and optimize cloud-based data pipelines, collaborate with Finance, BI, and Architecture teams, and contribute to governance and production-ready data products.

Qualifications

• 4+ years of relevant Data Engineer experience.
• Hands-on production-scale data solutions in Google Cloud Platform (GCP).
• 4+ years in data warehousing, cloud platforms, relational databases, and visualization tools.
• 4+ years working with structured and unstructured data in batch and real-time processing.
• Proficiency in Python, Java, or C# for data engineering.
• Experience building cloud-based data solutions with governance, CI/CD, and data quality checks.

Responsibilities

• Design and build scalable ETL/ELT pipelines on modern cloud tech.
• Develop and optimize data architectures and databases.
• Maintain database code and performance-optimized transformations.
• Contribute to CI/CD workflows using GitHub Actions.
• Collaborate with Finance, BI, and Data Science teams to deliver analytics-ready data.

Skills

GCP experience

Python

SQL

Spark

ETL pipelines

CI/CD (GitHub)

Tools

PySpark

Matillion

GitHub Actions

Job description

McKesson Corporation is seeking a Data Engineer for Finance Solutions on Google Cloud Platform (GCP) in Richmond, VA. The role focuses on migrating and modernizing Finance data platforms to GCP, building scalable data solutions, and supporting AI/ML initiatives within a cross-functional Finance Data & BI team.

You will design, implement, and optimize cloud-based data pipelines, collaborate with Finance, BI, and Architecture teams, and contribute to governance and production-ready data products.