Senior Data Engineer - Data Platform

Zipline • South San Francisco, CA

1d • Onsite

About The Position

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.

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.

Nice To Haves

• Experience with high-volume telemetry or sensor data, data observability, infrastructure-as-code, or safety-critical systems is strongly preferred.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed