Senior Analytics Engineer

Join Zipline, a pioneering logistics company, as a Senior Analytics Engineer. In this role, you will be responsible for the analytics infrastructure and datasets that inform operational decisions across the company. You will design and build Zipline's central data platform, ensuring accuracy, latency, and auditability for live logistics and regulated aviation workflows. This position requires a strong background in analytics engineering, data engineering, or software engineering, with a focus on production systems and data quality.

• Ownership of analytics infrastructure and datasets that inform operational decisions across the company.
• End-to-end management of analytics products, including defining success metrics, designing schemas, implementing ETL/ELT pipelines, and ensuring data correctness.
• Collaboration with cross-functional teams to fix upstream data quality issues, prioritize engineering tradeoffs, and coordinate ship schedules for schema changes.

• Comfortable making engineering tradeoffs: cost vs. latency vs. reliability, and driving cross-team decisions with engineers and ops owners
• Direct experience operating production systems under failure: you have seen systems break, led incident response, and implemented durable fixes and prevention measures
• Deep SQL expertise and production experience with Snowflake and dbt (or equivalent); able to author performant transformations and manage model versioning and deployments
• 7+ years of analytics engineering, data engineering, or software engineering experience with ownership of production systems; demonstrated history as a DRI accountable for mission-critical business outcomes
• Strong experience implementing automated data quality, anomaly detection, and incident response runbooks; able to quantify baseline and improvements (e.g., reduced incidence of errors by X%)
• Experience operating under strict correctness and latency SLAs in logistics, manufacturing, aviation, or other regulated operational environments; familiarity with auditability, lineage, and trace requirements
• Location & logistics: Bay Area-based and able to work on-site at least 3 days/week preferred; travel to hubs/factories ~10% annually; flexible for occasional early-morning or after-hours incident responses
• Education: bachelor’s degree in a quantitative field or equivalent experience
• Track record building semantic layers/governed metrics consumed by BI and AI systems, and designing data contracts with downstream SLAs
• Production Python experience for EL pipelines, validation, and automation; familiarity with Airflow or equivalent orchestration tools