As a Data Analytics Engineer, you will play a pivotal role in designing, implementing and owning data solutions for US Oncology data sets. You will be responsible for managing complex tasks in Databricks, building tools with Python, leveraging API systems and assisting in the management of internal data and data-related systems.
Requirements
- Design and Architecture: Develop and maintain areas including data models, data pipelines, and technical specifications to support business goals and analytics initiatives.
- Database Management: Leverage cloud systems such as Databricks for data processing, analytics, and machine learning tasks, ensuring optimal performance and cost efficiency.
- Data Integration: Design and implement processes for data integration within a medallion architecture to ensure seamless data flow across various systems.
- Performance Optimization: Continuously monitor and optimize data processes, ensuring high performance, reliability, and scalability.
- Data Strategy: participate in setting comprehensive data strategy goals for the team in alignment with business needs.
- Collaboration: Work closely with data engineers, stakeholders, and business partners to understand and/or communicate project requirements. Help influence projects to drive the best results related to data quality, architecture and management.
- Innovation: Stay current with emerging technologies and industry trends and recommend innovative solutions to enhance our data architecture.
- Technical Project Management: Drive technical projects forward by partnering with 2nd- and 3rd-party partners, including other internal McKesson teams.
Benefits
- Competitive compensation package
- Annual bonus
- Long-term incentive opportunities
- 401k Matching
- Retirement Plan
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