Senior Product Manager (Data Collection)

Kynetec is the global leader in agricultural and animal health market insights. We have a long history of market research expertise, specialising in animal health and nutrition, crop protection, farm machinery and equipment, seed / biotech and fertilisers.

Backed by a team of more than 850 market researchers, interviewers, data analysts, marketing scientists, research operation and data visualization specialists, our number one priority is to ensure that we deliver the highest-quality insight and foresight at the right time to enable our clients to confidently make the best decisions for their business. Across the globe, our employees are located across 28 major agriculture and animal health countries. Our coverage extends to major and niche sectors of our industry, where we regularly undertake research projects in more than 80 countries.

The Role

We are looking for a Sr. Product Manager to serve as the product leader for our market research data collection platform. This individual will lead all aspects of the platform’s evolution, including requirements definition, backlog ownership, coordination of vendor / enterprise software adaptation, and oversee the day-to-day in-house engineering development as Product Owner. This role is critical to improving the efficiency, quality and scalability of how we collect agriculture market research data.

Day to Day Tasks

• Own the product strategy and roadmap for data collection and processing workflows.
• Improve efficiency, automation, data quality, and compliance across the data pipeline.
• Design and optimize data collection workflows (surveys, panels, APIs, third-party and syndicated data).
• Define clear product requirements, user stories, and acceptance criteria for engineering teams.
• Manage and prioritize the product backlog based on business impact and customer needs.
• Partner closely with research, data, engineering, QA, and operations teams.
• Evaluate and manage third-party and enterprise data collection tools.
• Ensure reliable, scalable, and high-quality data flows for downstream analytics.
• Define standards, KPIs, and monitoring to support data accuracy, governance, and system performance.
• Drive continuous improvement, including automation and AI-assisted capabilities.

Requirements

Nice to Have