Data Architect

About the Role

Abnormal Security is seeking a Data Architect to lead the design and execution of scalable, secure, and intelligent data systems that power our business operations and decision-making. As part of the IT organization, you will define the architecture and strategy for our enterprise data ecosystem, with a focus on governance, quality, and usability. Your work will support cross-functional needs—including G&A, GTM, and product—enabling trusted data for analytics, AI, and business automation.

This is a foundational role for Abnormal’s data future. You’ll be responsible for implementing data governance frameworks, managing enterprise data models, and guiding the evolution of our cloud-native data infrastructure. The ideal candidate brings deep technical expertise, strong business acumen, and a passion for building systems that scale with excellence

Who you are

• A systems thinker who designs data architecture to be modular, scalable, and business-aligned.
• Obsessed with data quality and integrity—recognizing governance and MDM as enablers of speed and trust.
• Hands-on technical leader with experience in enterprise data platforms like Snowflake, dbt, and modern ETL tools.
• Someone who thrives in fast-paced environments with shifting priorities and complex stakeholder needs.
• Able to translate business needs into technical solutions while balancing flexibility, security, and compliance.
• An evangelist for data standards, best practices, and the long-term value of clean architecture.
• Naturally collaborative, empathetic, and driven by a desire to support teams through better data.
• Familiar with the demands of cybersecurity or high-growth startup environments.
• Committed to velocity, ownership, and continuous improvement as core ways of working.
• Passionate about the potential of AI and machine learning, and how clean data accelerates that future.

What you will do 

• Design and implement scalable, secure, and governed data architecture across enterprise systems.
• Establish and maintain enterprise data models, taxonomies, and metadata management standards.
• Lead the rollout of master data management (MDM) and data governance frameworks.
• Architect and optimize our Snowflake data warehouse and associated tools (e.g., dbt, Fivetran, Sigma).
• Define data integration patterns and ensure high data quality, consistency, and lineage.
• Collaborate across IT, GRC, GTM, and Product teams to support cross-functional data needs.
• Enable self-service analytics and AI/ML capabilities by structuring data for usability and accessibility.
• Monitor, document, and continuously improve data flows, storage, access, and compliance.
• Evaluate and integrate cloud-native data technologies, staying current with trends in data and AI.
• Mentor engineers and analysts on data modeling, system design, and governance best practices.

Must Haves 

• 7+ years of experience in data architecture, data engineering, or related roles in modern data environments.
• Expertise in cloud-based data warehousing (especially Snowflake), SQL, and data modeling techniques.
• Proven experience implementing enterprise data governance and MDM programs.
• Familiarity with data orchestration and transformation tools like dbt or Airflow.
• Strong understanding of metadata, lineage, and data quality frameworks.
• Deep knowledge of data security, compliance, and privacy in distributed environments.
• Demonstrated ability to work cross-functionally and translate business needs into technical architecture.
• Excellent written and verbal communication skills, with a bias for documentation and clarity.
• Experience leading data initiatives in cybersecurity or fast-growing startup settings.

Nice to Have 

• Exposure to AI/ML systems and a working knowledge of data readiness for AI applications.
• Experience integrating BI tools (e.g., Sigma, Looker, Tableau) with governed data models.
• Familiarity with data catalogs, lineage tools, or data mesh principles.
• Previous work with real-time or event-based data architectures.
• Certifications in Snowflake, cloud platforms (AWS, GCP, Azure), or data governance frameworks.

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