Engineering Manager - Data Engineering

TrendAI, the global AI security leader and enterprise business unit of Trend Micro, empowers organizations with full AI visibility and consolidated security that inspires confidence, drives innovation, and eliminates risk. At TrendAI, we’re always seeking exceptional talent; people who want to collaborate with the best and push boundaries together. Here, your work goes beyond building a career. You will help protect what matters and play a vital role in shaping a safer, more trustworthy AI-powered future.

AI Fearlessly.

• Lead a team of data engineers and software engineers responsible for the Security Ontology platform.
• Own the end-to-end delivery of the Ontology engineering roadmap, from technical planning and prioritization through implementation, production rollout, and ongoing operations.
• Translate product and platform strategy into clear engineering objectives, milestones, execution plans, and measurable outcomes.
• Establish effective engineering processes, development standards, code quality practices, and delivery metrics.
• Drive execution across multiple work streams, proactively managing dependencies, technical risks, resource allocation, and delivery commitments.
• Build a culture of ownership, accountability, technical excellence, and continuous improvement.
• Mentor engineering leads and senior engineers, develop technical talent, and build a scalable organization capable of supporting the platform's long-term growth.
• Balance near–term product delivery with foundational investments in architecture, reliability, performance, and developer productivity.

• Lead the development of a shared security ontology spanning endpoint, identity, cloud, network, email, threat intelligence, and other security domains.
• Establish canonical entities, relationships, attributes, semantic contracts, and domain modeling standards across security products.
• Drive ontology versioning, evolution, governance, and interoperability practices that support independent product development without fragmenting the shared knowledge model.
• Lead the definition and implementation of reusable patterns for mapping product‑specific schemas into the common semantic model.
• Deliver capabilities for entity resolution, relationship discovery, provenance, confidence scoring, and cross-product correlation.
• Partner with product engineering teams to onboard new security domains and evolve existing integrations as product schemas and capabilities change.

• Lead the design, implementation, and scaling of the Security Knowledge Graph and its supporting services.
• Deliver a production‑grade Ontology API and services layer that provides consistent semantic access to security knowledge for applications and AI agents.
• Drive the implementation of entity resolution, context retrieval, graph traversal, semantic search, knowledge enrichment, and reasoning capabilities.
• Define service abstractions that decouple applications and agents from underlying storage technologies and implementation details.
• Evaluate and guide the adoption of graph, vector, relational, and hybrid storage technologies based on scale, performance, reliability, and operational requirements.
• Establish service–level objectives and engineering practices for availability, latency, throughput, data consistency, and operational observability.
• Continuously improve platform scalability and cost efficiency as data volumes, product integrations, and agent workloads grow.

• Lead the engineering work required to integrate the Security Ontology and Knowledge Graph with AI agent memory and orchestration systems.
• Establish clear boundaries between…