2 days ago
Base Salary
$155k - $210k/yr
Responsibilities
- Own analytics products from success metrics and schema design through ETL/ELT implementation, testing, production operation, and incident resolution.
- Build production data models and pipelines for aircraft availability, delivery performance, inventory reconciliation, and other operational datasets.
- Implement automated data-quality checks, anomaly detection, rollback procedures, alert thresholds, remediation SLAs, and monitoring dashboards.
- Build semantic layers, governed metrics, documented data contracts, lineage, backward compatibility, and versioning for BI and AI consumers.
- Partner with Software, Hardware, Field Ops, and Manufacturing teams to resolve upstream data-quality issues and coordinate schema changes.
- Define and report KPIs including data-accuracy error rate, pipeline MTTR, consumer adoption, manual reconciliation reduction, and operational decisions enabled.
- Extend the internal AI analytics harness with evaluation tests, ground-truth datasets, explainability, auditability, and conservative fallback behaviors.
- Set standards for testing, dbt CI/CD, production monitoring, and runbooks; mentor, onboard, and review junior analytics engineers.
Requirements
- At least 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 preventative fixes.
- 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 with strict correctness and latency SLAs in logistics, manufacturing, aviation, or another regulated operational environment, including auditability and lineage.
- Strong experience with automated data quality, anomaly detection, incident response runbooks, and quantifying improvements.
- Ability to make cost, latency, and reliability tradeoffs and drive cross-team decisions with engineering and operations stakeholders.
- Bachelor’s degree in a quantitative field or equivalent experience.
- Ability to work in the Bay Area and preferably on-site at least three days per week, travel to hubs and factories approximately 10% annually, and support occasional early-morning or after-hours incidents.
Benefits
- Starting cash range is $155,000–$210,000 annually, with potential equity compensation, overtime pay, discretionary annual or performance bonuses, and sales incentives excluded from the base range.
- Benefits may include medical, dental, and vision insurance and paid time off.
- Bay Area, California location is preferred with on-site work at least three days per week.
- Occasional travel to operational hubs and manufacturing sites is required, approximately 10% annually.
- The role may require occasional early-morning or after-hours incident response.
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.