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# Data Engineer, Finance Data & BI

- **Company:** McKesson
- **Location:** Richmond, VA, United States
- **Experience:** Experienced
- **Salary:** $105,800.0 - $176,300.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, C Sharp (Programming Language), Cloud Computing, Databases, Continuous Integration, Data Architecture, Data Validation, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Structures, Data Systems, Data Visualization, Data Warehousing, Relational Databases, Dimensional Modeling, Github, JD Edwards EnterpriseOne, Python (Programming Language), Microsoft Software, Object-Oriented Software Development, Oracle (Applications), Application Data, SQL Stored Procedures, SQL Databases, Data Streaming, Teradata SQL, Unstructured Data, Data Processing, Google Cloud, Snowflake, Apache Spark, Pyspark, Data Analytics, Data Management, Stream Processing, Software Version Control, Data Pipelines, Databricks
- **Published:** July 6, 2026
- **Apply:** https://www.jofdav.com/jobs/58712161-data-engineer-finance-data-bi

## About the Role

* 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

## Description

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., 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


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