Senior Data Engineer - Austin, TX

Trend Micro•Austin, TX

1d•Hybrid

About The Position

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. This is a hybrid role and requires in-office presence at our Austin, TX office three days per week. We are building a next-generation AI-native security platform powered by a shared ontology and security knowledge graph. Our mission is to transform a collection of security products into a unified intelligence platform where agents can reason across identities, devices, cloud resources, threats, incidents, and business assets. As a Senior Data Engineer, you will play a critical role in building the data foundation that powers our ontology platform. You will design and implement large-scale ingestion pipelines, semantic mapping frameworks, entity resolution processes, and product integrations that convert siloed security data into connected knowledge. This role sits at the intersection of data engineering, cybersecurity, knowledge graphs, and AI systems.

Requirements

5+ years of experience in Data Engineering, Platform Engineering, or related fields.
Strong experience building large-scale distributed data systems.
Proficiency in Python and SQL.
Experience with modern data platforms such as: Apache Spark Apache Kafka Apache Flink Airflow Databricks
Experience designing and operating ETL/ELT pipelines at scale.
Strong understanding of data modeling and schema design.
Experience working with cloud platforms such as: AWS Azure GCP
Familiarity with REST APIs and event-driven architectures.

Nice To Haves

Experience with graph databases and knowledge graph technologies such as: Neo4j Amazon Neptune JanusGraph
Experience with ontology modeling, semantic technologies, or entity resolution systems.
Cybersecurity domain knowledge across endpoint, cloud, identity, network, or SIEM platforms.
Experience building data products that support AI, machine learning, or agentic systems.
Familiarity with graph analytics, relationship modeling, and knowledge representation.
Experience operating large-scale streaming architectures.

Responsibilities

Design and develop scalable pipelines that transform product-specific data models into a canonical ontology.
Build data ingestion frameworks for security telemetry, alerts, incidents, asset inventories, cloud resources, and identity systems.
Implement schema mapping and transformation processes between product databases and ontology entities.
Develop entity resolution and deduplication mechanisms across multiple security products.
Ensure data quality, lineage, consistency, and governance throughout the knowledge lifecycle.
Partner with product teams to onboard new security products into the ontology ecosystem.
Design reusable integration patterns and semantic contracts for product teams.
Build APIs, connectors, and streaming pipelines that publish product data into the ontology platform.
Drive standardization of cross-product data models and relationships.
Model entities and relationships that support graph-based reasoning.
Optimize graph ingestion and enrichment workflows.
Support creation of attack paths, asset relationships, risk models, and contextual security views.
Collaborate with ontology architects to evolve the platform's semantic model.
Build highly scalable distributed data pipelines processing billions of security events.
Develop observability, monitoring, and quality validation frameworks.
Implement batch and real-time data processing architectures.
Drive performance optimization, scalability, and reliability of ontology ingestion systems.
Enable AI agents to consume structured security knowledge through ontology APIs.
Contribute to data products that support agent reasoning, investigation, and automation.
Build enrichment pipelines that improve context available to autonomous security workflows.