Position open to candidates located in Brazil. Please submit your resume in English.

To learn more about our team and office culture in São Paulo, Brazil, visit the following links.Careers Page: https://www.knowbe4.com/careers/locations/sao-pauloGlassdoor: https://www.glassdoor.com/Location/KnowBe4-S%C3%A3o-Paulo-Location-EI_IE969384.0,7_IL[…]M_-C1lsxoZq7Cx8IriVE8MkrzuTmnJzqego77RAWZz9sqGt_55BflwYKpQegLinkedIn: https://www.linkedin.com/company/knowbe4/life/brazil/

As a Staff Data Engineer, you will serve as a technical lead and architectural pillar for the global data platform team. You will partner with engineering management and stakeholders to define the long-term architecture of our event-driven platform, establish agentic AI frameworks, and guide technical direction for the team.

• Partner with the Principal Data Engineering Manager to design and plan the platform's core architecture, selecting tools, technologies, and system patterns.
• Design systems and security guardrails to provide customers with agentic experiences while preventing data leakage across multi-tenant environments.
• Formulate data orchestration and modeling strategies to maximize business impact, enforcing querying best practices and architectural standards.
• Own the architecture and reliability of key domains within the data platform, contributing to org-wide standards, SLAs, and best practices that other teams build against.
• Work closely with Infosec, SRE, and executive leadership (Directors/VPs) to ensure platform initiatives align with company-wide compliance, security, and business goals.
• Guide Senior Engineers on complex design patterns, code quality, and adoption of modern data platform tools.

• ~5+ years of experience in data platform architecture and data engineering, with a track record of driving technical strategy and system design.
• Expert proficiency in Python, SQL, PySpark, and AWS data platform services.
• Solid understanding of data lake, data warehousing and analytics.
• Deep background in designing event-driven architectures operating at scale (thousands of events/sec).
• Practical knowledge or strong domain exposure to agentic AI workflows, LLM integration, and AI safety guardrails.
• Proven ability to translate complex business needs into data architecture and communicate technical roadmaps clearly to non-technical stakeholders.