Data Engineer, Finance Data & BI

McKesson offers competitive compensation and a hybrid work model: This Data Engineer role sits on the Finance Solutions team in Richmond, VA and focuses on designing, building, and maintaining scalable data pipelines and analytics-ready datasets for Finance Data & BI to support reporting, planning, forecasting, automation, and AI/ML within governed data environments. The position combines hands-on technical work with cross-functional collaboration across Finance, BI, and Data Product teams, and provides a hybrid schedule with in-office collaboration.

Responsibilities

• Tackle end-to-end data challenges across the full data stack, from advanced data wrangling with SQL, Python, Spark or similar, to delivering production‑scale, stakeholder‑ready data solutions.
• Architect new data solutions and reengineer existing architectures, including optimized data structures, relational databases, and database code.
• Build, test, and maintain robust ETL/ELT pipelines on modern cloud technologies to support analytics and AI/ML workloads.
• Develop and maintain database code, including stored procedures, functions, and performance‑oriented transformations.
• Create and maintain ETL processes and participate in CI/CD deployment workflows using GitHub Actions or similar tools.
• Design and optimize data models, such as dimensional modeling, within the Finance data environment.
• Optimize data architecture for performance, scalability, and cost efficiency across large financial datasets.
• Implement automated data quality checks, anomaly detection, and validation processes to ensure accuracy for downstream analytics and AI use cases.
• Ensure all data solutions meet financial governance and compliance standards, including SOX requirements.
• Collaborate with Finance teams (Accounting, FP&A), BI, and Data Product partners to translate complex business requirements 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

• Minimum of four years of relevant experience as a data engineer.
• Four or more years of hands-on experience with data warehouse solutions, cloud platforms, relational databases, and data visualization or dashboarding tools.
• Four or more years of experience working with structured and unstructured data in batch and real-time processing environments.
• Strong proficiency in object‑oriented programming languages such as Python, Java, or C#.
• Experience with Google Cloud Platform (GCP) is preferred.
• Proven enterprise experience building and optimizing cloud‑based data solutions, supporting business‑critical systems, and designing or supporting production‑scale AI/ML data pipelines.
• Experience with data governance by design, data warehousing and ETL best practices, and CI/CD with 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: $106,500 - $177,500 per year
• Annual bonus or long‑term incentive opportunities may be offered

Hybrid expectations

• This role is based in Richmond, VA with 3 to 5 days in the office each month.
• Candidates should reside within a reasonable commuting distance (within 60 miles of Richmond).
• Relocation assistance is not available for this role.

Compensation

• Base: $130,000 to $140,000
• 10% annual incentive