Data Engineer (Product & Customer Insights)

We are looking for a strong Data Engineer to be part of one of Torq's most strategic initiatives - building the context graph that powers our AI-SOC platform. Our team is building the context layer behind Torq's AI Agents, transforming raw customer data into meaningful, normalized context that enables smarter autonomous security decisions. This is a unique opportunity to join a brand-new, small, senior team with high ownership, strong product influence, and the chance to build foundational AI infrastructure from the ground up.

Responsibilities

Think like a product person - collaborate with stakeholders and consumers to design meaningful data models and continuously validate them against real customer data; Run POCs work end to end - connect a new data source, ingest and normalize it, correlate entities across systems, and demonstrate the value; Do the analysis that shapes the model - profile new data, measure coverage and accuracy, and find the gaps that matter to the product; Integrate identity, endpoint, and security data sources - learn how each system exposes its data and turn it into reliable, consistent models; Build and own scalable data pipelines, transforming raw data into clean, normalized, and well-tested models using Python, SQL, and dbt

Requirements

Analytical & product mindset: Able to investigate datasets, validate findings, and translate insights into scalable solutions; 4+ years of experience as a Data Engineer; Strong data engineering skills: Strong proficiency in SQL and Python, with experience in relational data modeling and integrating data from external APIs; Ownership: Independent and proactive, able to drive projects from raw data to production-ready solutions; Excellent communicator who thrives working across cross-functional teams; Experience working alongside AI agent systems and evaluating their outputs

Nice to have

Background in cybersecurity, including security operations, identity, endpoint, and alert data; Experience with modern data stack tooling, including orchestration frameworks such as Airflow, Dagster, or similar; Hands-on experience building and maintaining dbt models and tests in production pipelines; Experience modeling data using graph databases (Neo4j or similar)