Zipline
Senior Analytics Engineer
$155K–$210K
South San Francisco, California, USA +1 · Senior · Full-time · Posted Jul 23, 2026
- Location
- South San Francisco, California, USA
South San Francisco, California, United States - Experience
- 7+ years in analytics engineering, data engineering, or software engineering.
- Education
- Bachelor’s degree in a quantitative field or equivalent experience.
About the job
Owns production analytics infrastructure, datasets, and AI analytics safeguards that support operational decisions across Zipline’s logistics and regulated aviation workflows.
What you'll do
- Build and operate production data models and pipelines, with ownership of accuracy, freshness, validation, and incident response.
- Develop governed metrics, semantic layers, data contracts, and analytics products in partnership with engineering and operations teams.
- Extend the internal AI analytics harness with evaluation tests, ground-truth datasets, and auditable fallback behaviors; mentor analytics engineers.
What they ask for
- 7+ years of analytics engineering, data engineering, or software engineering experience, including ownership of production systems and mission-critical outcomes.
- Deep SQL expertise and production experience with Snowflake and dbt (or equivalent); production Python experience for pipelines, validation, and automation.
- Experience with orchestration tools such as Airflow or equivalent, and with governed metrics, semantic layers, and data contracts.
- Experience operating under strict correctness and latency SLAs in logistics, manufacturing, aviation, or other regulated operational environments.
Nice to have
- Bay Area-based and able to work on-site at least 3 days/week.
- Experience with auditability, lineage, trace requirements, and automated data quality or anomaly detection.
Pay
USD $155,000 - $210,000 starting cash range (base salary)
Tools
SQL, Snowflake, dbt, Python, Airflow
AI focus
Extend the internal AI analytics harness with evaluation tests, ground-truth datasets, and conservative fallback behaviors to make operational AI answers explainable and auditable.
Model-written summary · the original posting is authoritative