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McKesson Medical-Surgical (MMS) is seeking a highly skilled and motivated Data Engineer to join the Finance Solutions team. This is a critical individual contributor role within our Finance Data & BI organization.

The Data Engineer will design, build, and optimize scalable data pipelines and architectures that transform raw financial data into high‑quality, analytics‑ready, and AI‑enabled datasets. These datasets support strategic reporting, planning, forecasting, automation, and advanced analytics across the organization.

Reporting to the Director, Finance Data & BI, this Data Engineer will serve as an agile technical partner to Data Product, Architecture, Business Intelligence, Data Science, Finance Analysts, and business stakeholders. This role plays a key part in building the future‑state Finance data platforms at MMS, supporting enterprise analytics, AI/ML use cases, and sound financial governance.

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

Key Responsibilities

Data Engineering & Architecture

• Solve complex problems across the full data stack, from advanced data wrangling (SQL, Python, Spark, or similar) to delivering stakeholder‑ready, production‑scale data solutions

• Design new architectures and reengineer existing ones, including optimized data structures, relational databases, and database code

• Develop, construct, test, and maintain robust, scalable, and efficient 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 maintain ETL processes and contribute to CI/CD deployment workflows using GitHub Actions or similar tools

Optimization, Data Quality & Governance

• Implement and optimize data models (e.g., dimensional modeling) within the Finance data environment

• Optimize data architecture for performance, scalability, and cost efficiency across large financial datasets

• Design and implement automated data quality checks, anomaly detection, and validation processes to ensure accuracy and trust in downstream analytics and AI use cases

• Ensure all data solutions meet financial governance and compliance standards, including SOX requirements

Collaboration & Communication

• Partner closely 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

Minimum Requirements

• 4+ years of relevant experience

Preferred Critical Requirements

• 4+ years of technical and professional 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 experience in an enterprise environment with:

  • Building and optimizing cloud‑based data solutions

  • Supporting business‑critical systems

  • Designing or supporting production‑scale AI/ML data pipelines

  • Applying data governance by design

  • Data warehousing and ETL best practices

  • CI/CD and version control using GitHub

Additional Skills: (Nice to Have)

• Experience with PySpark

• Experience with Matillion or other modern ETL tools

• Working knowledge of core financial data concepts (general ledger, chart of accounts, financial reporting)

• Experience deploying SOX‑compliant data solutions in a regulated enterprise

• Experience with Oracle JD Edwards or familiarity with financial application data integrations and data flows

We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here.

Our Base Pay Range for this position

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McKesson is an Equal Opportunity Employer

McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson’s full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page.

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