Data Engineer

Role overview Build and mature the data pipelines, models, and warehouse behind a dataset of global threat patterns and street-level intelligence. You’ll combine hands-on data engineering with input into architecture, helping produce reliable information for analytics and security decision-making. The position is remote and limited to candidates based in the United States.

Responsibilities - Build, test, and maintain production ETL pipelines for structured and unstructured data, including enrichment with machine learning models. - Improve pipeline stability and performance, and investigate operational incidents. - Create data transformations, validation, and ongoing quality monitoring to keep datasets dependable and useful. - Design warehouse solutions and dimensional data models for reporting and analytics. - Establish data quality processes with monitoring, alerts, and remediation. - Contribute to architecture decisions and deliver improvements to the data platform in collaboration with data scientists, analysts, and engineers.

Requirements - At least 3 years building production data pipelines with modern data stack tools. - At least 3 years implementing and maintaining data warehouses and dimensional models. - Strong SQL and understanding of data modeling practices. - Proficiency in Python for data engineering. - Experience with workflow orchestration and automation tools. - Experience implementing data quality and governance practices, and collaborating across technical teams.

Nice to have - Geospatial data or large-scale dataset experience. - Web scraping experience. - Familiarity with machine learning and AI infrastructure or JavaScript.

Benefits and work setup Remote within the United States.