Senior Data Engineer - Data Platform

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.

Zipline builds, manufactures, and operates autonomous delivery systems that move essential goods where they are needed. 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 the teams building and operating Zipline’s delivery system.

• 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.

• 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.
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