Data Engineer - Seattle, WA

Nscale is the GPU cloud engineered for AI, providing high-performance infrastructure for AI-focused companies. The Data Engineer will design, build, and operate data foundations to support Nscale’s platform and internal operations, working closely with various teams to create reliable, scalable data products.

Responsibilities:

• Design and build scalable, reliable data pipelines that ingest data from infrastructure, platform services, and business systems
• Define data models and schemas that support operational workflows and use cases across the business, monitoring, and analytics
• Clean, transform and structure the data to create a digital twin of Nscale
• Implement permissioning and manage access and security of the Foundry implementation
• Create trusted datasets and metrics that power workflows and processes, internal tools, and customer-facing insights
• Enable self-serve analytics by establishing clear data contracts, documentation, and semantic layers
• Build use cases including but not limited to capacity planning, cost optimisation, reliability analysis, and customer reporting to drive our business forward
• Collaborate with Product and Commercial teams to translate real-world questions into robust data solutions
• Implement data quality checks, monitoring, and alerting to ensure data correctness and availability
• Codify data lineage, freshness, and consistency across systems
• Establish best practices around data versioning, access control, and governance appropriate for a fast-scaling company
• Continuously improve system resilience and observability
• Take end-to-end ownership of projects, from design through to production and iteration
• Help define standards, tooling, and ways of working for data at Nscale
• Contribute to technical decision-making as the company scales its platform and customer base
• Act as a thought partner to engineers and operators, not just a service function

Requirements:

• Deep, hands-on experience building in Palantir Foundry, including ontology modelling, pipeline development, API integration, and large-scale data platform design
• Strong proficiency in Python, with experience applying data engineering libraries and frameworks (e.g. Spark, PySpark, Dask, pandas) to work with large, complex datasets
• Familiarity with API-driven data integration, including REST, GraphQL, and Foundry Action APIs
• Practical experience working in Git-based development workflows, including code reviews, version control, and CI/CD pipelines
• Comfort working in ambiguous, early-stage environments where requirements evolve quickly
• Strong communication skills — able to explain data concepts clearly to technical and non-technical stakeholders
• A bias toward ownership, pragmatism, and shipping useful solutions
• Experience with cloud platforms (AWS, GCP, Azure) and infrastructure telemetry
• Familiarity with distributed systems, monitoring data, or usage-based billing data
• Experience supporting customer-facing data products or platforms