Key Responsibilities:

• Design, develop, and optimize data pipelines using Python / PySpark and Snowflake to process and analyze large datasets.
• Write complex SQL queries for data extraction, transformation, and loading (ETL) from multiple, often complex data sources ensuring consistency and accessibility across platforms
• Work with Databricks to build and maintain collaborative and scalable data solutions.
• Implement and manage CI/CD processes for data pipeline deployments to ensure seamless and efficient integration and deployment.
• Create and optimize data models that structure raw data into clean, organized formats suitable for business analysis and reporting.
• Implement and manage CI/CD processes for data pipeline deployments to ensure seamless and efficient integration and deployment.
• Work closely with data scientists, analysts, and business stakeholders to understand data needs, support data-driven projects, and enhance data accessibility.
• Implement data security protocols and ensure compliance with regulations (e.g., GDPR, HIPAA) to protect sensitive information.
• Ensure projects are delivered on schedule and within established timelines.
• Aid in the creation and maintenance of Standard Operating Procedures (SOPs). 
• Maintain clear documentation of data workflows, schemas, and data infrastructure, ensuring best practices are followed and enabling knowledge sharing within the team.

Education (minimum/desirable): Masters/ PhD in Computer Sciences / IT or other quantitative sciences

Experience / Skills:

• 7+ years’ experience in a Global company as a data steward, engineer, modeler or data scientist
• Business understanding of pharmaceutical industry and data standards. Domain experience in at least one of the following areas – a) Pharma R&D, b) Manufacturing, Procurement and Supply Chain and c) Marketing and Sales. Experience in working in Pharma / Life Science industry and US healthcare data is strongly preferred.
• Hand on experience in working with ETL process, and programming/scripting skills (Python) for data preparation and analysis. Experience in Master Data Management and data quality controls is a must.
• Proven experience with PySpark, including developing and tuning data processing applications.
• Advanced proficiency in SQL and experience in writing complex queries and optimizing them for performance on any ETL tool.
• Hands-on experience with Databricks, including notebooks, clusters, and integration with other data tools.
• Strong understanding of CI/CD pipelines and experience with tools such as Jenkins, GitLab CI/CD, or Azure DevOps.
• Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and related data services.
• Proactive attitude and ability to work independently, with excellent problem-solving skills and attention to detail
• Excellent communication and interpersonal skills with consulting and senior stakeholder engagement experience. Experience in Scrum Methodology is a plus.
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills, with the ability to work effectively in an Agile team environment.

Preferred Skills:

• Understanding of healthcare / life sciences domain data and know-how of pharma ecosystem
• Good to have knowledge on kedro framework.
• Understanding and applying effective data governance methods in an enterprise-wide initiative
• Understanding on AI / ML model building on Snowflake