Staff Data Engineer - Data Platform

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.
About the Role
We are looking for a Staff Data Engineer – Data Platform to help design and build the next generation of the Vision One Data Platform.
This is a highly technical, hands-on role focused on creating a modern data foundation that enables Vision One engineering teams to discover, govern, access, transform, and analyze data across the platform. You will work at the intersection of distributed databases, data engineering, data governance, semantic discovery, and self-service analytics.
A key part of the role is enabling dbt as a self-service data transformation and modeling capability across Vision One OLTP databases, while building the underlying architecture, governance, and data movement infrastructure required to do this reliably at scale.
You will work closely with platform, product, security, and data engineering teams to establish common data standards and infrastructure that can be reused across Vision One.
What You’ll Do
Design and build the Vision One Data Platform, including data ingestion, replication, transformation, governance, discovery, and serving capabilities.
Build scalable data pipelines and change-data-capture (CDC) architectures using technologies such as Datastream and similar streaming/replication technologies.
Enable self-service dbt across Vision One OLTP data sources, providing product teams with governed capabilities to transform, model, test, document, and expose their data without requiring the central data platform team to build every pipeline.
Establish patterns for safely extracting and replicating data from OLTP and distributed SQL databases into analytical and downstream data systems.
Design architectures around distributed SQL technologies such as YugabyteDB, CockroachDB, or similar platforms.
Work with technologies such as Databricks and modern analytical processing engines to support large-scale data processing and analytics.
Build a comprehensive data catalog and metadata foundation that enables teams and AI agents to understand what data exists, where it comes from, what it means, and how it can be used.
Develop semantic data discovery capabilities that allow engineers, analysts, applications, and AI agents to discover relevant data based on business meaning rather than only database/schema names.
Establish data governance standards covering ownership, lineage, schemas, quality, access control, retention, and lifecycle management.
Develop reusable data platform primitives and APIs that product teams can consume as self-service capabilities.
Build automated mechanisms for data quality, schema evolution, lineage, observability, and freshness monitoring.
Design for evolving product schemas and ensure the platform can accommodate continuous changes without creating excessive duplication or operational overhead.
Partner with security and platform engineering teams to establish appropriate fine-grained data access controls across databases, schemas, tables, and data products.
Help establish the source-of-truth architecture for Vision One data while minimizing unnecessary data duplication.
Provide technical leadership and architectural guidance to other data engineers and product engineering teams.
Drive engineering standards around reliability, scalability, performance, cost efficiency, and operational excellence.
What You Bring
Required:
8+ years of experience in data engineering, distributed systems, backend engineering, or a related field.
Strong experience designing and building large-scale data platforms and data pipelines.
Deep understanding of OLTP and OLAP architectures and the tradeoffs between them.
Hands-on experience with CDC, data replication, and streaming architectures.
Strong SQL expertise and experience working with relational databases at scale.
Experience with distributed SQL databases, such as YugabyteDB, CockroachDB, Spanner, or similar technologies.
Experience building data transformation workflows using dbt or similar data modeling frameworks.
Experience with modern analytical/data platforms such as Databricks, Spark, Trino, Snowflake, BigQuery, or equivalent.
Strong understanding of data governance concepts including metadata, lineage, data quality, ownership, access control, and data catalogs.
Experience designing systems that support self-service data consumption and transformation.
Strong software engineering fundamentals, including API design, testing, CI/CD, observability, and production operations.
Ability to work across organizational boundaries and influence architecture and engineering standards without relying solely on formal authority.
Preferred Qualification