Data Engineer, Finance Data & BI

McKesson is seeking a Data Engineer on the Finance Solutions team to design, build, and maintain scalable data pipelines and analytics-ready datasets for Finance Data & BI in a hybrid Richmond, VA role.

Responsibilities

• Tackle complex problems across the full data stack, from advanced data wrangling (SQL, Python, Spark, or similar) to delivering stakeholder-ready, production-scale data solutions
• Architect new designs and reengineer existing ones, optimizing data structures, relational databases, and database code
• Build, test, and maintain robust, scalable ETL/ELT pipelines using modern cloud technologies that support advanced analytics and AI/ML workloads
• Develop and maintain database code, including stored procedures, functions, and performance-optimized transformations
• Create and sustain ETL processes and contribute to CI/CD deployment workflows using GitHub Actions or similar tools
• Implement and optimize data models, including dimensional modeling, within the Finance data environment
• Optimize data architecture for performance, scalability, and cost efficiency across large financial datasets
• Design automated data quality checks, anomaly detection, and validation processes to ensure accuracy for downstream analytics and AI use cases
• Ensure all data solutions meet governance and compliance standards, including SOX requirements
• Collaborate with Finance teams (Accounting, FP&A), BI, and Data Product partners to translate complex business needs into scalable technical solutions
• Communicate technical concepts clearly to non-technical stakeholders, balancing innovation with operational risk and controls
• Enable trusted data environments required for forecasting models, scenario planning, and AI-driven insights
• Contribute to the strategic evolution of the Finance data platform by evaluating and piloting emerging tools and technologies

Requirements

• 4+ years of relevant experience as a Data Engineer
• 4+ years of hands-on experience with data warehouse solutions, cloud platforms, relational databases, and data visualization or dashboarding tools
• 4+ years of experience working with structured and unstructured data in batch and real-time data processing environments
• Strong proficiency in object-oriented programming languages such as Python, Java, or C#
• Demonstrated experience with Google Cloud Platform (GCP) preferred over other platforms (e.g., Snowflake, Databricks, Microsoft, Teradata)
• Proven enterprise experience including:
  • Building and optimizing cloud-based data solutions
  • Supporting business-critical systems
  • Designing or supporting production-scale AI/ML data pipelines
  • Embedding data governance by design
  • Data warehousing and ETL best practices
  • CI/CD and version control using GitHub

Technologies

• SQL
• Python
• Spark
• Google Cloud Platform (GCP)
• GitHub Actions
• GitHub
• Matillion
• PySpark
• Oracle JD Edwards
• Snowflake
• Databricks
• Teradata
• Java
• C#

Benefits

• Base pay range: USD 106,500 - 177,500 per yearly
• Annual bonus or long-term incentive opportunities may be offered

Hybrid expectations

• This position is based in Richmond, VA with 3 to 5 days in the office each month
• Preferred candidates must currently reside within a reasonable commuting distance (defined as within 60 miles of Richmond)
• Relocation assistance is not available for this role

Compensation

• Base: $130,000 to $140,000
• 10% Annual Incentive