Senior Data Engineer - Data Platform

Zipline is the world’s largest and most experienced drone delivery service, 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 across four continents. With over 140 million commercial autonomous miles safely flown, Zipline strengthens supply chains, reduces congestion, and redefines access to healthcare, consumer products, and food globally.

About You And The Role

Zipline builds, manufactures, and operates autonomous delivery systems that move essential goods where they need to go. As a Senior Data Engineer on the Data Platform team, you will build cloud data foundations that turn application and operational telemetry into trusted inputs for delivery-system decisions, product analytics, and ML. You will own the data backbone across ingestion, orchestration, lakehouse and warehouse systems, and shared engineering standards. Success means making operational telemetry and downstream datasets dependable, discoverable, performant, and cost-conscious for teams building and operating Zipline’s delivery system.

Responsibilities

• Design and operate cloud services for data ingestion, transformation, validation, and serving across batch and near-real-time pipelines.
• Build orchestration patterns and shared libraries, templates, CI checks, and golden paths that standardize dataset publishing and transformations.
• Own lakehouse and warehouse foundations, including table management, schema evolution, partitioning, workload isolation, access patterns, and lifecycle management.
• Establish data contracts, metadata, naming, versioning, lineage, freshness, quality checks, and service objectives tied to operational and business impact.
• Diagnose distributed-system bottlenecks and improve throughput, query performance, concurrency, storage efficiency, and infrastructure or warehouse cost.
• Partner with service, telemetry, embedded, analytics, data science, and ML teams; lead technical reviews and align teams on durable platform interfaces and tradeoffs.

Requirements

• 5+ years of professional software or data engineering experience, including production systems built primarily with Python or Rust.
• Strong distributed-systems and data-systems foundations, with hands-on experience building and operating pipelines and data infrastructure at scale.
• Experience with cloud infrastructure and production operations, ideally AWS and Kubernetes, plus workflow orchestration tools such as Prefect or Airflow.
• Experience with modern data warehouses, preferably Snowflake, and lakehouse table formats, data modeling, and transformation workflows such as dbt.
• Demonstrated judgment on cost, performance, and reliability, including production guardrails, backfills, idempotency, retries, safe deployments, and data-outage runbooks.
• Clear technical communication and influence. Experience with high-volume telemetry or sensor data, data observability, infrastructure-as-code, or safety-critical systems is strongly preferred.

Pay

The starting cash range for this role is $160,000–$200,000. The final cash pay will depend on experience, qualifications, skills, working location, and projected impact. The total compensation package may also include equity compensation, discretionary bonuses, benefits such as medical, dental, and vision insurance, and paid time off.

This is a full-time role based in South San Francisco.