Data Engineer - Platform 1 (P1)

Zipline•South San Francisco, CA

22d

About The Position

Zipline builds and operates fleets of delivery drones to get critical and life-saving products to the people who need them, fast. Our software team powers this system by building and maintaining scalable digital infrastructure that enables our world-class distribution centers to serve customers with speed and precision. We're looking for a Data Engineer to build and scale the data infrastructure powering Zipline's Platform 1 (P1) — the order management and fulfillment system behind our automated logistics service in Africa. This platform acts as our internal CRM, order management system, coordinating order fulfillment, inventory management, and customer communication across an expanding network of countries and delivery use cases. You'll design and maintain the data pipelines, warehousing solutions, and analytics infrastructure that enable data-driven decision-making across operations, product, and business teams. You'll work closely with software engineers, data analysts, and operations teams to ensure reliable data availability, quality, and insights that drive operational efficiency and strategic growth. If you're someone who thrives in a fast-paced, collaborative environment and is excited about building data systems that power real-world impact in the world's largest autonomous logistics network, we'd love to hear from you.

Requirements

4+ years of experience as a Data Engineer or Backend/Data-focused Software Engineer working on production data systems.
Strong proficiency with Python , SQL , and at least one modern data orchestration tool (Airflow, Prefect, Dagster, etc.).
Hands-on experience designing data models, ELT/ETL pipelines, and warehouse architectures using Snowflake, BigQuery, Redshift, or equivalent platforms.
Experience working with distributed systems, APIs, microservices , and event-driven data flows.
Familiarity with dbt , schema versioning, metadata management, and tested data transformations.
An ability to work with ambiguous operational requirements and turn them into scalable, reliable data systems.
Comfort collaborating with cross-functional partners from engineering, operations, regulatory teams, and commercial teams.
Clear and thoughtful communication skills, with a strong commitment to documentation and shared ownership.
Comfort working in a fast-paced environment with evolving requirements and ambiguous constraints.
Clear, concise communication skills and ability to translate technical concepts for non-technical stakeholders.
Strong problem-solving skills and attention to detail.

Nice To Haves

Experience supporting logistics, supply chain, healthcare, aviation, or robotics systems.
Background working with government system integrations, compliance reporting, or regulated industries.
Knowledge of CI/CD workflows, containerization (Docker, Kubernetes), or infrastructure-as-code.
Experience with streaming data (Kafka, Pub/Sub, Kinesis) or real-time data architectures.

Responsibilities

Design, build, and maintain robust data pipelines that ingest, clean, transform, and synchronize data across P1 operational systems, internal tools, and external partners’ platforms
Develop and optimize data models that support analytics, reporting, and machine learning applications across the P1 platform
Own and evolve Zipline’s P1 data architecture including warehouse models, data marts, and integration schemas ensuring maintainability, clarity, and alignment with Zipline’s data standards.
Collaborate with product, operations, and platform engineers to translate real-world logistics requirements into data solutions that improve service reliability, regulatory reporting, and operational decision-making
Build and maintain ETL/ELT processes that support order tracking, inventory management, delivery performance, and customer behavior analytics
Create and manage data infrastructure that scales across geographies, complexity, and evolving business needs
Build data quality monitoring and alerting frameworks to proactively detect pipeline failures, data drift, schema changes, or anomalous behaviors across geographies.
Partner with data analysts and business stakeholders to translate analytical needs into robust data solutions
Lead by example in code reviews, technical discussions, and knowledge sharing to support team growth

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