Staff Data Engineer

Verkada Verkada · Enterprise · Bayoffice · Data

Verkada is seeking a Staff Data Engineer to develop core enterprise data warehouse infrastructure, data models, and pipelines. This role involves leading the design and implementation of automated data pipelines, collaborating with stakeholders to define data requirements, and driving the data engineering roadmap. The engineer will also be responsible for platform-wide engineering standards, data security, integrity, and regulatory compliance, and will provide leadership and mentorship.

What you'd actually do

  1. Architect, engineer and maintain efficient, scalable warehouse infrastructure that facilitates high-quality, accurate insights and reporting.
  2. Lead, design, implement and manage automated data pipelines from various data sources including databases, API endpoints, business systems, and data lakes.
  3. Lead collaboration across departments to develop bronze, silver, and gold data models, enforcing business alignment and data governance.
  4. Partner with Finance, Sales, Marketing, Product, and HR stakeholders to define data pipeline sources, data modeling requirements, and data quality standards.
  5. 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.

Skills

Required

  • Python
  • SQL
  • cloud warehouses (BigQuery, Snowflake, Databricks)
  • DBT
  • data lakes
  • Apache Iceberg
  • Delta Lake
  • Apache Hudi
  • Airflow
  • Dagster
  • Fivetran
  • operational databases
  • API endpoints
  • business systems

Nice to have

  • data observability
  • data quality platforms (BigEye, Monte Carlo, Great Expectations)
  • vector databases for Generative AI use cases
  • Gen AI agents to optimize development workflows within data engineering
  • Gen AI agents for providing support for business intelligence related inquiries

What the JD emphasized

  • 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.