Senior Data Architect & Revenue Analytics Engineer

McKesson Corporation; Tennessee; Hybrid; USD 134,000 - 223,000; Full time

Benefits offered by this job

Hybrid work arrangement

Competitive compensation

Job summary

McKesson Corporation in the United States is seeking a Lead Data Engineer / Architect to own the data architecture, pipelines, models, and automation behind revenue and performance analytics.

You will design cloud-based data environments, integrate Salesforce and other platforms, and drive AI-enabled revenue intelligence initiatives while ensuring accuracy, security, and scalability across analytics and financial close processes.

Qualifications

• 7+ years of data engineering, analytics engineering, or data architecture experience.
• Expert knowledge of ETL/ELT, data modeling, and dashboards for sales/revenue reporting.
• Advanced SQL, Python, and scripting for large-scale data processing.
• Experience with Databricks, Snowflake, and cloud data platforms.

Responsibilities

• Design, build, and support a cloud-based data environment for revenue and performance data.
• Define Revenue Operations data architecture and cloud data models.
• Own data lifecycle from collection to dashboards and reports.
• Automate data flows for month-end and quarter-end close.
• Develop data models and ETL/ELT pipelines; integrate Salesforce/Outreach/finance systems.
• Lead AI-enabled revenue intelligence use cases with governance and security.

Skills

SQL

Python

Databricks

Snowflake

Azure Data Factory

Power BI

Tableau

ETL/ELT

Data Modeling

API Design

Education

Bachelor's degree

Tools

Databricks

Snowflake

Azure Data Factory

Job description

McKesson Corporation in the United States is seeking a Lead Data Engineer / Architect to own the data architecture, pipelines, models, and automation behind revenue and performance analytics.

You will design cloud-based data environments, integrate Salesforce and other platforms, and drive AI-enabled revenue intelligence initiatives while ensuring accuracy, security, and scalability across analytics and financial close processes.