Job description

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. 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. Minimum Qualifications 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. Preferred Qualifications 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. #LI-REMOTE #LI-LN1 Axonius is committed to fair and equitable compensation packages. A candidate’s salary will be based on qualifications and relevant experience. In addition to a competitive salary, our packages include stock options, attractive benefits, and an annual bonus. Annual Salary Range (bonus and equity included in addition to the below numbers) $171,000 — $190,000 USD About Axonius: Axonius transforms cyber asset intelligence into actionability. With the Axonius Asset Cloud, customers preemptively tackle high-risk and hard-to-spot threat exposures, misconfigurations, and overspending. The integrated platform brings together data from every system in an organization’s IT infrastructure to optimize mission-critical risk, performance, and cost measures via actionable intelligence. Covering cyber assets, software, SaaS applications, identities, vulnerabilities, infrastructure, and more, Axonius is the one place to go for Security, IT, and GRC teams to continuously drive actionability across the organization. Cited as one of the fastest-growing cybersecurity startups, with accolades from CNBC, Forbes, and Fortune, Axonius supports millions of assets for leading customers across industries and around the world. Axonius makes it a priority to invest in our people with competitive compensation and benefits, growth opportunities, community-building, and so much more. Learn more about benefits at Axonius . At Axonius we support a diverse and inclusive workplace and believe in equal employment opportunity. We welcome people of different backgrounds, experiences, abilities and perspectives to apply. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, religion, age, sex, gender identity, national origin, sexual orientation, citizenship, marital status, disability, or Veteran status. Axonius participates in E-Verify. To learn more, please review the E-Verify Participation Poster (PDF) and the Right to Work Poster (PDF) . By submitting your application to us, you acknowledge that your personal data will be processed in accordance with our Global Job Candidate Privacy Notice.

Requirements

Must-haves

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.
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.
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.

Nice-to-haves

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.
Axonius makes it a priority to invest in our people with competitive compensation and benefits, growth opportunities, community-building, and so much more. Learn more about benefits at Axonius .

Application prep

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Keywords to mirror

aiagileawsmlsqlpythonapijiramachine learninggo

Platform: greenhouse
Effort: quick

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