Senior Data Engineer

Who you are

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

What the job involves

• 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