Principal Data Engineer, Axonius

• 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

Desirable

• 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
• 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