Senior Full Stack Software Engineer, Data

Zipline
South San Francisco, CA
Hybrid

Who this role is best for

Strong fit for candidates with deep data engineering experience who can own analytics infrastructure and operate in regulated environments, with a preference for on-site work in the Bay Area and a track record of cross-functional collaboration.

Best fit for

  • Candidates with 7+ years of analytics engineering experience and a history of owning mission-critical systems
    — “7+ years of analytics engineering, data engineering, or software engineering experience with ownership of production systems”
  • Individuals who have led incident response and implemented durable fixes in production systems
    — “Direct experience operating production systems under failure: you have seen systems break, led incident response”
  • Professionals with expertise in SQL, Snowflake, and dbt who can build semantic layers and governed metrics
    — “Deep SQL expertise and production experience with Snowflake and dbt (or equivalent); able to author performant transformations”
  • Candidates who can design data contracts and manage versioning for BI and AI tools
    — “designing data contracts with downstream SLAs”

Things to consider

  • On-site work in South San Francisco is preferred, with at least 3+ days per week required
    — “Location: Bay Area, CA (on-site 3+ days/week) preferred”
  • Occasional travel to hubs and manufacturing sites is a regular annual commitment
    — “occasional travel to hubs and manufacturing sites required (~10% annually)”

How to stand out

  • Highlight production-grade data pipeline ownership and measurable improvements in data accuracy
    — “ship production-grade data models and pipelines that must meet strict accuracy, latency, and auditability requirements”
  • Demonstrate experience with automated data quality checks and incident response runbooks
    — “Strong experience implementing automated data quality, anomaly detection, and incident response runbooks”
  • Showcase ability to build semantic layers and governed metrics for BI and AI tools
    — “Build semantic layers, governed metrics, and documented data contracts consumed by BI and AI tools”
  • Emphasize cross-functional collaboration with teams like Field Ops and Manufacturing
    — “Partner closely with the Software, Hardware, Field Ops, and Manufacturing”
  • Demonstrate ability to define and measure KPIs tied to operational impact
    — “Instrument and measure impact: define and report KPIs such as data-accuracy error rate”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • accurate and timely data
  • mission-critical insights
  • production-grade data models
Typical background
full stack software engineerdata engineer

Skills & requirements

Required

Full Stack DevelopmentData ModelingEtl/elt PipelinesProduction-grade Data ModelsAccuracyLatencyAuditabilitySoftware EngineeringOperations

Preferred

Zipline's Central Data PlatformMission-critical InsightsFlight SchedulingDispatchCustomer Delivery

Stack & domain

Full Stack Software EngineeringData AnalyticsEtl/elt PipelinesData ModelsData CorrectnessData Freshness SlasIncident ResponseAircraft Availability SignalsDelivery Performance MetricsFactory Yield DatasetsSafety Event LineageInventory Reconciliation ReportsManufacturing And Ops LeadsGoverned Delivery-performance MetricsRaw Events LineageAutomated Inventory Reconciliation ReportsProblem SolvingAdversity HandlingRecord Breaking SpeedsFirst Principles ThinkingCross-functional CollaborationOperational DecisionsLive LogisticsRegulated Aviation WorkflowsDrone DeliveryAutonomous LogisticsHealthcareConsumer ProductsFoodRetail ProductsSupply ChainsCongestion ReductionTime ManagementGlobal OperationsLogisticsManufacturingInventoryFlight SchedulingDispatchCustomer DeliveryRegulatory Compliance

About the role

Original posting from Zipline

About Zipline

Zipline is the world’s largest and most experienced drone delivery service. We are on a mission to serve all humans equally by ensuring access to food, medicine and essential goods anytime, anywhere. We design, build, and operate the world’s largest autonomous logistics system, delivering critical supplies quickly and reliably. Today, Zipline operates on four continents, makes a delivery somewhere in the world every 30 seconds, and has completed millions of deliveries to date, including blood, vaccines, medical supplies, food, and retail products. 

Our customers include the world’s largest and most prominent healthcare systems, governments, retailers, restaurants and global businesses who rely on us to save lives, reduce emissions, increase economic opportunity, and provide delivery from point A to point B as fast as possible. The drone is only 15% of what we’ve built to enable seamless, reliable, global operations.

Our system strengthens supply chains, reduces congestion, and gives people time back. With more than 140 million commercial autonomous miles safely flown, Zipline is redefining access to healthcare, consumer products, and food across the globe.

We operate at a global scale and are looking for practical problem solvers who thrive on real-world challenges and rapid growth. Our team is motivated by building systems that have a direct, meaningful impact on people’s lives and by scaling the future of logistics. We are seeking people who sculpt from first principles, enjoy facing adversity, and can do the impossible at record breaking speeds.

About You and The Role 

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 Full Stack Software Engineer, Data, 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 Software Engineering Analytics 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).

What You'll Do 

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.

What You'll Bring

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.

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.

Experience operating under strict correctness and latency SLAs in logistics, manufacturing, aviation, or other regulated operational environments; familiarity with auditability, lineage, and trace requirements.

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.

Education: bachelor’s degree in a quantitative field or equivalent experience.

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

The starting cash range for this role is $155,000 - $210,000. Please note that this is a target, starting cash range for a candidate who meets the minimum qualifications for this role. The final cash pay for this role will depend on a variety of factors, including a specific candidate's experience, qualifications, skills, working location, and projected impact. The total compensation package for this role may also include: equity compensation; overtime pay; discretionary annual or performance bonuses; sales incentives; benefits such as medical, dental and vision insurance; paid time off; and more.

Zipline is an equal opportunity employer and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws or our own sensibilities.

We value diversity at Zipline and welcome applications from those who are traditionally underrepresented in tech. If you like the sound of this position but are not sure if you are the perfect fit, please apply!

Voluntary Self-Identification

For government reporting purposes, we ask candidates to respond to the below self-identification survey. Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiring process or thereafter. Any information that you do provide will be recorded and maintained in a confidential file.

As set forth in Zipline ’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

Source: Zipline careers

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