Principal Data Engineer

At Axonius, a Most Loved Workplace® certified employer in the Technology space, Transform asset intelligence into intelligent action across your technology footprint.

<p>An AI-first global organization operating in a fast-paced environment requires a central data engineering engine, and this role serves that need by using Agile methodologies to architect highly scalable, secure, and reliable enterprise data platforms. As a Principal Data Engineer (IC5), you will drive substantial impact on the division’s reputation and technical effectiveness, independently assessing complex scenarios to develop the methods, techniques, and evaluation criteria for obtaining mission-critical results. You will provide deep expertise in cloud data infrastructure and pipeline architecture across GTM, Product, CX, Finance, Legal, and People domains, making critical architectural decisions within broadly defined AI first best practices. Operating as a highly visible technical leader, you will facilitate collaboration across employees, clients, and senior executives to deliver scalable data solutions before the business even asks for them.</p>

<h2><strong>Responsibilities</strong></h2>

<ul>

<li>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.</li>

<li>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.</li>

<li>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.</li>

<li>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

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