Staff Software Engineer - 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.

• 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 Qualifications

• Experience building a company-wide or platform-level Data Platform rather than individual product pipelines.
• Experience with Datastream, Debezium, Kafka, or similar CDC/streaming technologies.
• Experience with YugabyteDB or CockroachDB in production environments.
• Experience operating Databricks or similar distributed data-processing platforms.
• Experience designing data catalogs, semantic layers, knowledge graphs, or ontology-driven data discovery.
• Experience building platforms that expose data to AI/agentic applications.
• Experience with data mesh, data products, or domain-oriented data architectures.
• Experience implementing fine-grained authorization for data platforms.
• Experience managing large-scale telemetry, security, observability, or other high-volume data.
• Experience balancing data quality, query performance, platform reliability, and infrastructure cost.

What Success Looks Like

In this role, you will help establish a Data Platform where:

• Vision One data can be discovered through a common semantic layer, rather than requiring teams to understand every underlying database and schema.
• Product teams can use dbt as a governed self-service capability across their OLTP data.
• Data lineage, ownership, quality, and access policies are built into the platform rather than managed manually.
• Data can move reliably from distributed OLTP systems into analytical and downstream systems through standardized CDC and streaming patterns.
• Product teams can consume platform capabilities without repeatedly building their own ingestion, transformation, governance, and discovery infrastructure.
• The platform provides a trusted source of truth while minimizing unnecessary copies of data.
• The resulting data foundation can support not only traditional analytics and applications, but also AI and agentic experiences across Vision One.

Engineering Leadership

As a Staff Engineer, you will be expected to operate beyond the boundaries of a single team. You will shape architecture, establish technical standards, mentor engineers, drive cross-team adoption, and solve ambiguous problems that span multiple products and systems.

You should be comfortable going deep into implementation details while simultaneously thinking about the long-term architecture of a large-scale, multi-product security data platform.