Hiring: Senior Analytics Engineer Company: Zipline Location: South San Francisco, CA Job Posted Time: 2026-09-10 10:33:45 Employment Type: On-site Target Skills & Keywords : Airflow, CI/CD, ELT, ETL, Python, SQL, Snowflake, dbt About the job Experience: •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. Required Skills: •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. Qualifications: •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. •Production Python experience for EL pipelines, validation, and automation; familiarity with Airflow or equivalent orchestration tools. •Track record building semantic layers/governed metrics consumed by BI and AI systems, and designing data contracts with downstream SLAs. •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%). •Comfortable making engineering tradeoffs: cost vs. latency vs. reliability, and driving cross-team decisions with engineers and ops owners. •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. •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. •What Else You Need To Know Compensation: •$155,000 - $210,000 / year Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!