Senior Business Intelligence Engineer

Requirements

• 5+ years as a Data Engineer, analytics/BI Engineer or in a similar role at a senior IC level
• Advanced SQL and deep proficiency in data modeling, schema design, and query optimization
• Strong grasp of data warehouse architecture and modern modeling methodologies
• Hands-on experience applying software engineering practices to data work:
  Git, CI/CD, code review, automated testing, monitoring
• Proficiency with cloud-based data platforms (Redshift, Snowflake, Databricks, Big Query, or similar)
• Strong communicator who is comfortable working across both business stakeholders and engineering teams
• (Desirable) Experience building or extending internal AI/LLM-powered tooling for analytics or developer workflows
• (Desirable) Expertise with Spark (PySpark or Scala) and big data environments such as Delta Lake or Data Lake
• (Desirable) Production-level proficiency with dbt and the modern data stack
• (Desirable) Production-grade Python — beyond scripting, into shared libraries, services, or pipelines
• (Desirable) A platform engineering mindset — building tools and abstractions that make others more productive

• As a Senior BI Engineer on our Business Intelligence team, you'll sit at the intersection of data, business, and engineering — designing the data systems, models, and AI-powered tooling that scale how Zip Recruiter operates and makes decisions
• You'll partner with Rev Ops, Decision Science, Product, and Engineering teams, working with technologies like Spark, Databricks, and Kubernetes to process billions of records daily
• Architect across business and engineering:
  Lead the design and implementation of data systems that serve both our operational business stakeholders (Rev Ops, Decision Science) and our product and engineering teams
• Extend our internal AI platform:
  Contribute to and help shape the future with work that compounds across every analysis we run at Zip
• Set the engineering bar for data:
  Bring software engineering rigor to data work: testing, CI/CD, code review, version control, observability and evangelize these practices across the team
• Build and own data models:
  Translate complex, cross-domain business questions into robust, well-tested, reusable data sets and models
• Partner with product engineering:
  Collaborate directly with engineering teams to embed analytical and data-modeling capabilities into product systems and pipelines
• Mentor and uplevel:
  Coach teammates, raise the technical bar, and help establish standards that scale beyond any single project