Senior Analytics Engineer

• The P2 Analytics Platform team’s mission is to supercharge every function at Zipline with the data they need to optimize every day
• We design and build Zipline’s central data platform to provide mission-critical insights for Zipline’s wide variety of business units, from manufacturing and inventory, to flight scheduling and dispatch, to customer delivery, and more
• As a Senior Analytics Engineer, you are the owner of analytics infrastructure and datasets that directly inform operational decisions across the company, ensuring planes launch on time, inventory is accurate, and exception workflows keep customers and regulators satisfied
• This role sits at the intersection of analytics, software engineering, and operations: you will ship production-grade data models and pipelines that must meet strict accuracy, latency, and auditability requirements for live logistics and regulated aviation workflows
• Location: Bay Area, CA (on-site 3+ days/week) preferred, with occasional travel to hubs and manufacturing sites required (~10% annually)
• You will report to the Analytics Engineering Lead and be the DRI for at least one cross-functional analytics product (e.g., delivery performance metrics, factory yield datasets, or safety event lineage)
• Own end-to-end analytics products: define success metrics, design schemas, implement ETL/ELT pipelines, test for accuracy, and operate datasets in production. Be accountable for data correctness, freshness SLAs, and incident response until resolved
• Deliver the first 6-month roadmap items as DRI (examples): consolidate multi-source aircraft availability signals into a single fleet health data set; build governed delivery-performance metrics with lineage to raw events; automate inventory reconciliation reports used by manufacturing and ops leads daily
• Implement rigorous validation: automated data-quality checks, anomaly detection, and rollback procedures with measurable alert thresholds and agreed remediation SLAs with ops owners
• Build semantic layers, governed metrics, and documented data contracts consumed by BI and AI tools; enforce backward-compatibility and versioning so downstream consumers do not break
• Partner closely with the Software, Hardware, Field Ops, and Manufacturing to fix upstream data quality issues at the source; prioritize engineering tradeoffs (cost, latency, reliability) and coordinate ship schedules for schema changes
• Instrument and measure impact: define and report KPIs such as data-accuracy error rate, pipeline MTTR, consumer adoption, reduction in manual reconciliation time, and operational decisions enabled (e.g., % improvement in on-time deliveries attributable to analytics changes)
• Extend Zipline’s internal AI analytics harness: add evaluation tests, ground-truth datasets, and conservative fallback behaviors to ensure AI answers used in ops are explainable and auditable
• Mentor and elevate the team: set standards for testing, dbt CI/CD, production monitoring, and runbooks; onboard and review work from junior analytics engineers
• Success in the first 6 months will look like: production delivery of at least one mission-level dataset with end-to-end lineage and data-quality checks; establishment of SLA targets and monitoring dashboards, and measurable reduction in a manual reconciliation or troubleshooting pain point owned by ops

Comfortable making engineering tradeoffs: cost vs. latency vs. reliability, and driving cross-team decisions with engineers and ops ownersDirect experience operating production systems under failure: you have seen systems break, led incident response, and implemented durable fixes and prevention measuresDeep SQL expertise and production experience with Snowflake and dbt (or equivalent); able to author performant transformations and manage model versioning and deployments7+ 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 outcomesStrong 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 requirementsLocation & 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 responsesEducation: bachelor’s degree in a quantitative field or equivalent experienceTrack record building semantic layers/governed metrics consumed by BI and AI systems, and designing data contracts with downstream SLAsProduction Python experience for EL pipelines, validation, and automation; familiarity with Airflow or equivalent orchestration tools