Your key responsibilities:
- 7+ years of experience in data engineering, including at least 2+ years in a leadership or delivery oversight role.
- Strong hands-on experience with Python, SQL, Spark/PySpark, and tools like Databricks, Airflow, Snowflake, Azure Data Factory.
- Demonstrated ability to manage vendor teams and ensure quality delivery across geographically distributed teams.
- Strong communication and stakeholder management skills; experience working with commercial business leads in pharma or life sciences.
- Basic understanding of pharmaceutical commercial data at both country and global levels, including CRM data, sales performance data, field force activity, and syndicated datasets.
- Familiarity with privacy and compliance frameworks relevant to healthcare data (HIPAA, GDPR, GxP).
- Experience working in Agile, cross-functional product teams.
Preferred Qualifications:
- Direct experience in pharma commercial domains such as market access, promotional analytics, or omnichannel customer engagement.
- Knowledge of HCP/HCO identity resolution, customer 360 strategies, or AI-based targeting and segmentation use cases.
- Exposure to MLOps workflows and how data pipelines support AI product deployment at scale.
EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets.