Data Product Engineering Lead

integrity of data by implementing robust data validation and monitoring mechanisms. Partner with extended team and roll out strategies including data cataloging, self-service, access controls, and documentation. Work with DevOps and IT teams to ensure the reliability, scalability, and security of data systems. Stay updated on emerging technologies, tools, and trends in data engineering, and drive their adoption where applicable. Evaluate and recommend tools, platforms, and frameworks that enhance the efficiency and scalability of the data engineering function. Apply Agile and DevOps practices. Ensure platform requirements are met per design and architecture. Collaborate with ZTD Infrastructure and Information Security members to evolve technology architecture across the enterprise. Salary: $261,248.00. We offer a competitive and comprehensive benefit package, which includes healthcare, dental coverage, and retirement savings benefits along with paid holidays, vacation and

disability insurance. Bachelor’s degree in Computer Science, Electrical/Electronics Engineering, or related and 10 years of experience required. Telecommuting may be permitted. When not telecommuting, must report to worksite. To apply, email your resume to Helen.Ljubicich@zoetis.com and reference job #9406064

Zoetis, Inc. Define and implement the vision, strategy, and roadmap for the data engineering function in alignment with organizational goals. Establish best practices, frameworks, and standards for data engineering processes. Collaborate with the data platform and architecture team to evolve data platform architecture and maturity. Lead, mentor, and manage a team of data engineers, fostering professional growth and collaboration. Design and build scalable, reliable, and secure data pipelines to collect, process, and store large datasets from diverse sources. Oversee the design and implementation of scalable and efficient data pipelines to ingest, process, and manage data from multiple sources. Collaborate with stakeholders to design efficient solutions for data integration, transformation, and enrichment. Partner with data scientists, analysts, and business teams to deliver data solutions that support analytics and decision-making. Ensure the accuracy, consistency, and