2 months ago
Base Salary
$155k - $210k/yr
Responsibilities
- Own end-to-end analytics products by defining metrics, designing schemas, implementing ETL/ELT pipelines, testing accuracy, and operating production datasets.
- Deliver roadmap initiatives such as fleet health datasets, governed delivery-performance metrics, and automated inventory reconciliation reports.
- Implement automated data-quality checks, anomaly detection, rollback procedures, alert thresholds, and remediation SLAs.
- Build semantic layers, governed metrics, documented data contracts, backward-compatible schemas, and versioned downstream interfaces.
- Partner with Software, Hardware, Field Ops, and Manufacturing teams to resolve upstream data-quality issues and coordinate schema changes.
- Define and report operational KPIs including data-accuracy error rate, pipeline MTTR, consumer adoption, and reduced manual reconciliation time.
- Extend the internal AI analytics harness with evaluation tests, ground-truth datasets, and conservative fallback behaviors.
- Set standards for testing, dbt CI/CD, production monitoring, and runbooks while mentoring and reviewing junior analytics engineers.
Requirements
- 7+ years of analytics engineering, data engineering, or software engineering experience owning production systems and mission-critical business outcomes.
- Experience operating production systems through failures, leading incident response, and implementing durable fixes and prevention measures.
- Deep SQL expertise and production experience with Snowflake and dbt or equivalent tools.
- Production Python experience for EL pipelines, validation, and automation, with familiarity with Airflow or equivalent orchestration tools.
- Experience building semantic layers and governed metrics for BI and AI systems and designing data contracts with downstream SLAs.
- Experience working under strict correctness and latency SLAs in logistics, manufacturing, aviation, or other regulated operational environments.
- Experience with automated data quality, anomaly detection, incident response runbooks, auditability, lineage, and trace requirements.
- Ability to make cost, latency, and reliability tradeoffs and drive cross-team decisions with engineering and operations owners.
- Bachelor’s degree in a quantitative field or equivalent experience.
- Ability to work in the Bay Area and on-site at least three days per week is preferred, with approximately 10% annual travel and occasional early-morning or after-hours incident response availability.
Benefits
- Target starting cash compensation includes a range of $155,000–$210,000.
- Potential benefits include equity compensation, overtime pay, discretionary annual or performance bonuses, medical insurance, dental insurance, vision insurance, and paid time off.
- Bay Area, California location with on-site work preferred at least three days per week.
- Occasional travel to hubs and manufacturing sites is required, approximately 10% annually.
About Zipline
Zipline designs, builds, and operates autonomous drone delivery systems for healthcare, retail, and public-sector customers. The privately held company runs end-to-end logistics—aircraft, software, and fulfillment—to deliver blood, vaccines, medicines, and consumer goods. Founded in 2014 and headquartered in South San Francisco, Zipline operates across multiple continents; notable work includes Rwanda’s national blood deliveries and U.S. home delivery pilots with Walmart.