Z
ZipRecruiter
Senior Business Intelligence Engineer
Skills
SQL QueriesBig DataPythonSnowflakeDatabricksGitData AnalysisAIScaladbtPySpark
1 month ago
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Important information
Salary:
$125,000 – $165,000 / year
Location:
United States
Work mode:
Remote
Published on:
26 September 2026
What they need
Who you are
- 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, BigQuery, or similar)
- Strong communicator who is comfortable working across both business stakeholders and engineering teams
- Experience building or extending internal AI/LLM-powered tooling for analytics or developer workflows
- Expertise with Spark (PySpark or Scala) and big data environments such as Delta Lake or Data Lake
- Production-level proficiency with dbt and the modern data stack
- Production-grade Python — beyond scripting, into shared libraries, services, or pipelines
- A platform engineering mindset — building tools and abstractions that make others more productive
What the job involves
- Design the data systems, models, and AI-powered tooling that scale how ZipRecruiter operates and makes decisions, partnering with RevOps, Decision Science, Product, and Engineering teams
- Lead the design and implementation of data systems that serve both our operational business stakeholders (RevOps, Decision Science) and our product and engineering teams
- Contribute to and help shape the future of our internal AI platform, impacting every analysis we run
- Bring software engineering rigor to data work: testing, CI/CD, code review, version control, observability and evangelize these practices across the team
- Translate complex, cross-domain business questions into robust, well-tested, reusable data sets and models
- Collaborate directly with engineering teams to embed analytical and data-modeling capabilities into product systems and pipelines
- Coach teammates, raise the technical bar, and help establish standards that scale beyond any single project
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