Director of Data Engineering

Title: Director of Data EngineeringLocation: Can be remote (candidates in the Bay Area, CA are highly preferred)The RoleWe are seeking a visionary Director of Data Engineering to lead our global data organization. Reporting directly to the Head of AI, you will be the primary architect of our enterprise data strategy, owning the end-to-end platform that powers everything from edge-device telemetry and autonomous systems to complex manufacturing operations.As the "Head of Data" for the organization, you will bridge the gap between core engineering and business operations, ensuring our data infrastructure is scalable, secure, and inherently AI-ready. This is a critical leadership role that requires a balance of high-level technical architecture and strategic organizational management across global borders.Key ResponsibilitiesPlatform Ownership: Own the vision, roadmap, and execution for the enterprise-wide data platform, specifically focused on a high-performance Databricks ecosystem.Global Leadership: Lead and scale a multi-disciplinary data team across North America and international hubs (including Eastern Europe and Asia), fostering a culture of technical excellence.AI & ML Foundation: Partner with the AI organization to build the data pipelines and infrastructure required for computer vision, predictive modeling, and agentic AI systems.Operations & Manufacturing: Support the integration of real-time industrial signals and telemetry to power preventative maintenance and production optimization.Governance & Security: Define and enforce global data governance, privacy, and security standards for sensitive user and proprietary hardware data.Executive Liaison: Act as the primary technical voice for data-driven decision-making across the executive team, ensuring data investments translate into measurable product value.Required QualificationsTech Sector Expertise: 10+ years of leadership experience within high-growth, modern technology environments (e.g., Robotics, IoT, SaaS, or Advanced Hardware).Databricks Mastery: Deep, hands-on architectural experience with the Databricks Lakehouse platform, including Delta Lake, Unity Catalog, and Spark optimization.Global Team Management: Proven track record of managing and growing engineering teams across multiple time zones and international markets.Scale & Complexity: Experience owning a data platform at massive scale (petabytes of data) and managing the transition from legacy architectures to modern, microservices-oriented stacks.Technical Literacy: A deep understanding of the machine learning lifecycle (MLOps) to ensure data infrastructure effectively supports AI development.Preferred QualificationsIndustrial/Manufacturing Experience: Prior experience in manufacturing, supply chain, or industrial robotics is a significant advantage.