AbbVie's "Winning with Data" (WWD) capability in International Commercial Business is driving the transformation of the organization into a data and insights driven decision-making organization and is seeking a talented Data Engineer to join our data engineering team. In this role, you will play a critical part in designing, building, and maintaining data solutions using Palantir Foundry to support our data-driven initiatives.
Requirements
- Palantir Foundry
- PySpark, Python, and SQL
- Data Modeling
- Git, Jira, Confluence
- AWS
- Experience with cloud platforms, big data platforms and one or more general purpose programming languages, including but not limited to: Python, SQL, Spark, Amazon Web Services, Typescript and Javascript
- Experience with DevOps principals and version control (Git preferred)
Responsibilities
- Planning, building, and running enterprise class information management solutions on Palantir Foundry
- Designing, developing, testing, and maintaining data pipelines to support Analytics projects within Commercial.
- Design, implement, and maintain scalable Functions in Palantir Foundry
- Building data products and service processes which perform data transformation, metadata extraction, workload management and error processing management.
- Develop and optimize ETL processes to ensure timely and accurate data delivery to various business applications.
- Implementing standardized, automated operational and quality control processes to deliver accurate and timely data and reporting.
- Adhering to best practices for coding, testing, and designing reusable code/component.
Other
- Collaborate with cross-functional teams to understand data requirements and translate them into robust and efficient solutions.
- Collaborating with Data Analysts and Scientists to enable streamlined data flow for the Commercial Analytics capabilities across brands.
- Ensuring appropriate security and compliance policies are followed for information access and dissemination.
- Defining and applying information quality and consistency business rules throughout the data processing lifecycle.
- Collaborating with information providers to ensure quality data updates are processed in a timely fashion.
