Principal Data Engineer

Axonius is a cybersecurity company that transforms cyber asset intelligence into actionable insights. The Principal Data Engineer will architect scalable, secure, and reliable enterprise data platforms, lead data engineering standards, and develop cloud-based pipelines and AI agents across business domains.

Responsibilities:

• Formulate and execute the technical architecture strategy for global ETL/ELT pipelines, streaming frameworks (such as AWS Kafka), and infrastructure-as-code (Terraform) deployments to support enterprise-wide AI/ML initiatives
• Serve as the principal technical authority for the data engineering work group, establishing development standards, code review practices, and evaluation criteria to ensure high pipeline availability and data freshness
• Architect, deploy, and maintain autonomous AI agents (e.g., Finance Analyst, Data Analyst agents) utilizing LLMs like Claude and Gemini, integrating them directly into the modern data stack for automated insights
• Independently assess high-risk architectural challenges across GTM, Finance, Product, and other operational domains, making definitive decisions on data warehousing design, upstream schema drift mitigation, and data lake scalability
• Partner with IT, Security, and Product infrastructure teams on highly specialized governance and architecture matters, including data asset correlation, normalization of disparate source schemas, and the enforcement of global RBAC and PII data masking standards
• Implement automated data pipeline monitoring, alerting, and automated cleansing mechanisms at the ingestion tier to proactively isolate and fix broken fields or source schema drifts
• Partner with Analytics Engineering to ensure the stability, scalability, and optimal performance of underlying production-ready repositories feeding the enterprise semantic layer

Requirements:

• 10+ years of experience in data engineering, cloud data architecture, or specialized infrastructure roles, with a proven track record of delivering technical leadership for engineering work groups
• Advanced proficiency in SQL and software engineering programming languages, specifically Python, for pipeline automation, data scripting, and AI agent orchestration frameworks
• Extensive experience designing, provisioning, and tuning production-grade cloud environments using Terraform to manage complex warehouse and networking configurations
• Proven experience independently architecting real-time streaming topologies, high-volume event data ingestions, and custom API-driven connector/adapter frameworks
• Experience delivering data infrastructure iteratively within a fast-paced environment utilizing Agile management tools like Jira
• Strong hands-on expertise architecting, optimizing, and managing multi-cluster enterprise data warehouses specifically within Snowflake
• Background managing cross-functional technical data requirements and standardizing architectures across GTM, Finance, Product, CX, Legal, and People divisions
• Deep experience designing and scaling advanced data platform solutions optimized for machine learning training pipelines, Vector DBs, and operational AI consumption
• Experience creating organizational methods, techniques, and governance frameworks to automate data cleansing, quality monitoring, and system resilience at scale
• Experience deploying and managing multi-region cloud data infrastructure architectures within AWS enterprise environments