Design and develop data pipelines, workflows, and data transformation processes.
Integrate various data sources to ensure seamless data flow between systems.
Optimize data processing and storage for performance and cost-efficiency.

Design, implement, and maintain database systems, including PostgreSQL and Azure databases.
Monitor and optimize database performance.
Implement robust backup and recovery strategies.

Implement and manage continuous integration and continuous deployment (CI/CD) pipelines.
Use Git for version control and collaboration.

Work closely with infrastructure teams to ensure seamless integration and deployment.
Engage with business partners to understand requirements and deliver solutions in a timely and cost-effective manner.

Demonstrate excellent problem-solving abilities and attention to detail.
Identify and resolve issues promptly within SLA guidelines, providing ongoing support to business users.

Design and develop data solutions using Azure Data Factory & Data Bricks – 40%
Oversee ETL/ELT processes including management and maintenance of pipelines. Extract, transform, and load data efficiently – 20%
Cross-team collaboration and learning new technologies to stay up-to-date – 20%
DevOps & Agile methodologies – 10%
Commercial business knowledge across systems such as SAP-ERP, Salesforce, Vistex-Rebates, Callidus, Five9, and home-grown master data management tools – 10%

Interact with business stakeholders to gather integration requirements, understand business processes, and ensure alignment with organizational goals.
Collaborate with implementation partners responsible for deploying, configuring, or maintaining integrated solutions within the Zoetis IT landscape.
Coordinate with developers and team members to implement integration solutions, share knowledge, and resolve technical challenges.

1.5–6.5 years of overall experience in Data Engineering.
Hands-on experience with Azure Data Factory and Data Bricks.
Expertise in Python, database management, and ETL/ELT concepts.
Strong analytical skills, excellent communication abilities, and a proactive approach to problem-solving.
Knowledge of data analysis, data modeling, data profiling, and architecting data analytic solutions.
Experience with DevOps and Agile methodologies.
CI/CD Pipelines: Implement and manage continuous integration and deployment.
Version Control: Use Git for version control and collaboration.
Agile Practices: Apply Agile methodologies for project management and delivery.

Data Engineer - Commercial