Data Engineer

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, turning raw operational signals into reliable 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