Senior Full Stack Software Engineer, Data

Zipline • South San Francisco, CA

1d • $155,000 - $210,000 • Onsite

About The Position

The P2 Analytics Platform team builds Zipline’s central data platform, providing mission-critical insights to business units across manufacturing, inventory, flight scheduling and dispatch, customer delivery, and other functions. The Senior Full Stack Software Engineer, Data owns analytics infrastructure and datasets that inform operational decisions, supporting reliable aircraft launches, accurate inventory, and effective exception workflows.

This role combines analytics, software engineering, and operations. The engineer delivers production-grade data models and pipelines with strict accuracy, latency, and auditability requirements for live logistics and regulated aviation workflows. The position is based in the Bay Area, with on-site work preferred at least three days per week and occasional travel to hubs and manufacturing sites, approximately 10% annually. The role reports to the Software Engineering Analytics Lead and serves as the directly responsible individual for at least one cross-functional analytics product.

Success in the first six months includes delivering at least one mission-level dataset with end-to-end lineage and data-quality checks, establishing SLA targets and monitoring dashboards, and measurably reducing an operations-owned manual reconciliation or troubleshooting burden.

Requirements

• At least 7 years of analytics engineering, data engineering, or software engineering experience, including ownership of production systems and a history of accountability for mission-critical business outcomes.
• Direct experience operating production systems through failures, leading incident response, and implementing durable fixes and preventive measures.
• Deep SQL expertise and production experience with Snowflake and dbt or equivalent, including performant transformations, model versioning, and deployments.
• Production Python experience for EL pipelines, validation, and automation.
• Familiarity with Airflow or an equivalent orchestration tool.
• Experience building semantic layers and governed metrics for BI and AI systems, and designing data contracts with downstream SLAs.
• Experience operating under strict correctness and latency SLAs in logistics, manufacturing, aviation, or another regulated operational environment.
• Familiarity with auditability, lineage, and trace requirements.
• Strong experience implementing automated data quality, anomaly detection, and incident-response runbooks, with the ability to quantify baselines and improvements.
• Ability to assess cost, latency, and reliability tradeoffs and drive cross-team decisions with engineers and operations owners.
• Bay Area-based and able to work on-site at least three days per week preferred; able to travel to hubs and factories approximately 10% annually and accommodate occasional early-morning or after-hours incident responses.
• Bachelor’s degree in a quantitative field or equivalent experience.

Responsibilities

• Own analytics products end to end, including defining success metrics, designing schemas, implementing ETL/ELT pipelines, validating accuracy, and operating production datasets.
• Be accountable for data correctness, freshness SLAs, and incident response through resolution.
• Deliver roadmap items such as consolidating aircraft availability signals into a fleet-health dataset, building governed delivery-performance metrics with lineage to raw events, and automating inventory reconciliation reports.
• Implement automated data-quality checks, anomaly detection, rollback procedures, alert thresholds, and remediation SLAs with operations owners.
• Build semantic layers, governed metrics, and documented data contracts for BI and AI tools; maintain backward compatibility and versioning for downstream consumers.
• Partner with Software, Hardware, Field Ops, and Manufacturing teams to address upstream data-quality problems and coordinate schema-change schedules.
• Make and coordinate engineering tradeoffs involving cost, latency, and reliability.
• Define and report impact KPIs, including data-accuracy error rate, pipeline MTTR, consumer adoption, reduced manual reconciliation time, and operational decisions enabled.
• Extend Zipline’s internal AI analytics harness with evaluation tests, ground-truth datasets, and conservative fallback behaviors so operational AI answers are explainable and auditable.
• Mentor and onboard junior analytics engineers and establish standards for testing, dbt CI/CD, production monitoring, and runbooks.

Benefits

• Equity compensation may be included in the total compensation package.
• Overtime pay may be included in the total compensation package.
• Discretionary annual or performance bonuses may be included in the total compensation package.
• Sales incentives may be included in the total compensation package.
• Medical, dental, and vision insurance.
• Paid time off.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior