Security Data Engineer

Role Overview

The Security Data Engineer is responsible for building and operating the data foundation that powers security monitoring, reporting, and analytics at Nebius.
This role focuses on designing reliable ETL pipelines, modeling security and infrastructure data, and enabling operational and strategic visibility through dashboards and analytical datasets. The role works closely with Security Engineers, Analysts, and central Data teams to ensure security data is trusted, scalable, and actionable.

Key Responsibilities

Data Engineering & ETL (core responsibility)

• Design, build, and maintain scalable ETL pipelines for security and infrastructure data using Azure Data Services and DBT stack.
• Develop and optimize PostgreSQL-based data models and warehouse structures to support security analytics.
• Integrate data from multiple domains (cloud infrastructure, security tooling, internal systems).

Analytics & Reporting Enablement

• Build and maintain datasets and data models for security operations, risk monitoring, and leadership reporting.
• Partner with security stakeholders to translate reporting needs into well-structured analytical models.

Data Quality, Reliability & Operations

• Ensure data quality, completeness, performance, and reliability across pipelines and datasets.
• Define and monitor warehouse KPIs (latency, load success, freshness).
• Contribute to data governance, documentation, lineage, and CI/CD practices for data workflows.

Key Qualifications & Experience

Must-have

• Strong SQL expertise (PostgreSQL), including schema design and performance optimization.
• Hands-on experience with Azure Data Services (like Blob Storage).
• Python for data transformation and automation.
• Solid understanding of data warehousing, ETL best practices, and analytical data modeling.
• Experience delivering dashboards or analytical datasets in Power BI, Tableau (or similar).

Preferred

• Exposure to security or infrastructure data (e.g. Defender, Sentinel, CVE/CVSS).
• Experience with time-series or anomaly-focused analytics.
• Familiarity with Git and CI/CD for data workflows.
• Experience working with cross-functional stakeholders in a global environment.

Competencies & Behavioral Traits

• Strong ownership mindset for data quality and reliability
• Ability to work independently within defined objectives
• Pragmatic problem-solving in ambiguous, data-heavy environments
• Clear communication with both technical and non-technical stakeholders
• Collaboration-first approach within a global, cross-functional organization