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OpenAI

Data Engineer, Monetization Data Platform

Mountain View$230k–$385kfulltimemidAdded today

About this role

Build and operate large-scale data pipelines and platforms that power monetization decisions across product, finance, and GTM teams. Own end-to-end systems from instrumentation through modeling, quality controls, and delivery to downstream consumers, working across complex financial and operational data domains.

What you'll do

  • Design and operate streaming and batch data pipelines processing product, financial, and operational data from multiple sources
  • Develop canonical data models and reusable products for pricing, billing, payments, revenue, and general ledger domains
  • Establish data accuracy, completeness, freshness, lineage, and auditability guarantees
  • Build platform frameworks and capabilities to improve developer productivity for monetization features
  • Partner with Product, Finance, Accounting, and GTM teams to define data contracts and instrument new monetization products
  • Lead technical design of complex cross-functional projects and improve observability of critical data workflows

What they're looking for

  • Data pipeline architecture and design
  • Python, Java, or Scala programming
  • Data modeling and distributed systems
  • Data quality, governance, and observability
  • SQL and data transformation frameworks
  • Workflow orchestration and scheduling
  • Streaming systems (Kafka, Pub/Sub, etc.)
  • Cross-functional collaboration and communication
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OpenAI

OpenAI builds AI infrastructure and products, including large-scale data center campuses for AI computing and generative AI applications for enterprise customers. The company is hiring civil engineers, project engineers, electrical design engineers, data center R&D engineers, and AI deployment engineers to expand its infrastructure capabilities and help customers deploy AI solutions.

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Likely interview questions

  • Describe a complex data pipeline you've built end-to-end—what were the key challenges around data quality and lineage?
  • How do you approach designing data models when you have conflicting requirements from multiple downstream teams?
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