Data Engineer, Finance Data & BI

McKesson•Richmond, VA

1d•Hybrid

About The Position

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.

Requirements

4+ years of relevant experience
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

Nice To Haves

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

Responsibilities

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
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
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