Staff Data Engineer

Who We Are Verkada is transforming how organizations protect their people and places with an integrated, privacy-sensitive AI-powered platform that includes solutions for video security, access control, air quality sensors, alarms, intercoms, and visitor management. We’ve got serious momentum in the market: more than 30,000 customers (including 100+ of the Fortune 500), a $5.8B valuation , more than $1 billion in annualized bookings, and backing from CapitalG, Sequoia Capital, General Catalyst, Felicis Ventures, Next47 and more. Physical AI is one of the most consequential technology shifts of our time, and Verkada is at the center of it. You can look at all kinds of communities to see our platform’s impact in the world. It's the retailer that uses our agentic AI to deter theft before it happens. The warehouse that uses AI-powered alerts to make sure its team is protected on the floor with proper PPE. The school that’s alerted to a threat in real-time and triggers a lockdown in seconds, not minutes. We’re rapidly scaling this impact: today, more than 2 million Verkada devices are deployed across 170+ countries. About the Role As a member of our Data Platforms and Analytics Team and reporting directly to the Head of Data, you will be responsible for developing the core enterprise data warehouse infrastructure, data models, and pipelines at Verkada. We aim to provide a single reporting source of truth for enterprise data with clear business data definitions to empower internal Finance, Sales, Marketing, Product and HR teams to make informed data driven decisions. Our strategy emphasizes automation, scalable architecture, and accuracy, while providing iterative improvements over time. We are committed to a thriving in-office culture. This role requires you to be onsite at our HQ in San Mateo, CA. What You'll Do Architect, engineer and maintain efficient, scalable warehouse infrastructure that facilitates high-quality, accurate insights and reporting. Lead, design, implement and manage automated data pipelines from various data sources including databases, API endpoints, business systems, and data lakes. Lead collaboration across departments to develop bronze, silver, and gold data models, enforcing business alignment and data governance. Partner with Finance, Sales, Marketing, Product, and HR stakeholders to define data pipeline sources, data modeling requirements, and data quality standards. Partner with the Head of Data to build and drive the data engineering roadmap — translating business priorities, technical debt, and platform gaps into a sequenced, milestone-driven plan that aligns with business objectives. Own the entire project lifecycle, moving initiatives from initial design through to production leveraging development standards such as Github PR reviews and Jira sprint board management. Drive platform-wide engineering standards: code quality, testing frameworks, CI/CD practices, data modeling conventions, and documentation, raising the bar across the entire data engineering team. Create and deploy strategies to maintain data security, integrity, and regulatory compliance. Provide leadership and guidance to grow and mentor future members of the data engineering team. What You Bring Bachelor's or Master's degree in Computer Science or a related technical field. Minimum of 10 years of professional data engineering experience. Advanced skill in Python and SQL. Expertise with cloud warehouses such as BigQuery, Snowflake, or Databricks leveraging DBT as a data modeling framework. Expertise in managing data lakes with open source file formats such as Apache Iceberg, Delta Lake or Apache Hudi Proven track record in constructing automated pipelines using Airflow, Dagster, Fivetran from various operational databases, API endpoints, business systems, and data lakes. Expert-level proficiency in SQL and Python is required. Experience building / managing data observability and data quality platforms such as BigEye, Monte Carlo and G