Ebay AI DataOps Lead 

Netherlands, North Holland, Amsterdam 

439105401

You will not just support AI development. You will help define how it operates, scales, and delivers value. Your work will directly influence model quality, operational efficiency, and the ways AI enhances user experience across eBay.

• Team Development and Leadership: Build and lead a high-functioning team of analysts and data engineers. Create a collaborative environment focused on delivery, accountability, and skill growth.
• Data Ownership and Lifecycle Management: Own the creation, management, and evolution of datasets used across company’s AI initiatives. Design structured processes for data acquisition, annotation, enrichment, and storage. Partner with internal teams and external providers to ensure the right data is collected, accurately labeled, and available at the right time.
• Labeling at Scale: Design and operate scalable labeling pipelines that serve a wide range of AI use cases. Define task structures, prepare data for annotation, and ensure consistent labeling quality through strong guidelines and validation processes. Collaborate closely with third-party providers to align on expectations, manage throughput, and monitor data quality across the full lifecycle of the engagement. Treat labeling not as an isolated task, but as a strategic capability that feeds directly into model success.
• Cross-functional Collaboration: Work with data scientists, engineers, and product managers to align data operations with AI goals. Translate research and product needs into data plans and workflows.
• Operational Strategy: Create work plans that reflect team priorities and long-term objectives. Adapt quickly to changing needs while keeping the team focused on outcomes that matter.
• Budget Oversight: Manage budgets tied to external partnerships and workforce resources. Make cost-effective decisions that support operational goals without compromising quality.
• Process, Standards and Compliance: Establish and maintain clear standards for how data is collected, structured, stored, and used across initiatives. Ensure all practices meet internal policy, legal, and privacy requirements, especially around the handling of sensitive or regulated information. Maintain high levels of data quality by enforcing consistency, managing taxonomies and ontologies, and ensuring datasets remain well-organized, traceable, and usable at scale.
• Analytics and Insight Development: Lead ongoing analysis efforts to extract insight from data that informs product direction, model performance, and operational decision making. Build a foundation for analytics as a core capability within the team, not just a reactive function.
• Metrics and Reporting:

• Team Leadership: Three to five years of experience managing teams. Comfortable hiring, coaching, and enabling team members to deliver high-quality work through clear direction, support, and collaboration.
• Tools and Technical Fluency: Proficient in Python for data processing, automation, and scripting tasks. Skilled in efficient data querying and retrieval using SQL. Comfortable working with large-scale systems such as Hadoop, and experienced in using Tableau and similar tools to build structured reporting and analytics workflows. Able to design data pipelines that are reliable, maintainable, and scalable.
• Dashboard and Reporting Design: Able to build reporting systems that serve engineering, research, and business audiences. You know how to turn raw data into dashboards that are reliable, insightful, and action-ready.
• Quality and Attention to Detail: You are thorough in how you work with data, documentation, and process. You catch inconsistencies early, verify accuracy before moving forward, and ensure standards are consistently met across workflows and deliverables.
• Clarity in Communication and Problem Solving: