Data Engineer

nscaleoperationsukltd; United States; Remote; USD 130,000 - 195,000; Full time

Benefits offered by this job

Remote-friendly

Equity

Career progression

Job summary

nscaleoperationsukltd is seeking a data engineer to build the data foundations for an AI-focused GPU cloud platform. You will work across operations, infrastructure, platform engineering, product, and commercial teams to shape how data is collected, modelled, served, and trusted.

Responsibilities include designing scalable pipelines, modelling data schemas, implementing security, and delivering trusted datasets for internal tools and customer insights.

Qualifications

• Experience building on enterprise data platforms (e.g., Palantir Foundry) including ontology modelling and large-scale pipeline design.
• Strong Python skills with Spark, PySpark, Dask, or pandas for large datasets.
• Familiarity with API-driven data integration (REST, GraphQL, action-style APIs).
• Experience with Git-based development workflows, reviews, version control, and CI/CD pipelines.

Responsibilities

• Design and build scalable data pipelines ingesting from infrastructure, platform services, and business systems.
• Define data models and schemas to support workflows, monitoring, and analytics.
• Clean, transform, and structure data for platform operations and insights.
• Implement data access controls, permissions, and security for the data platform.
• Create trusted datasets and metrics powering internal tools and self-serve analytics.
• Establish data quality checks, monitoring, lineage, and governance in a fast-scaling environment.

Skills

Palantir Foundry ontology modelling

Python data engineering (Spark PySpark

API data integration REST GraphQL

Git CI/CD workflows

Data governance & security

Stakeholder communication

Tools

Git

CI/CD tooling

Job description

Role overview

A data engineering position focused on building the data foundations for an AI-focused GPU cloud platform. The role is described as high-impact and early-stage, with broad cross-functional collaboration across operations, infrastructure, platform engineering, product, and commercial teams. The successful hire will help shape how data is collected, modelled, served, and trusted across the organisation.

Responsibilities

• Design and build scalable, reliable data pipelines that ingest information from infrastructure, platform services, and business systems.
• Define data models and schemas to support operational workflows, monitoring, and analytics across the business.
• Clean, transform, and structure data into a coherent digital representation of platform operations.
• Implement permissioning, access control, and security for the data platform.
• Build trusted datasets and metrics that power internal tools, customer-facing insights, and self-serve analytics through clear data contracts and documentation.
• Implement data quality checks, monitoring, alerting, lineage tracking, and governance practices suited to a fast-scaling environment.

Requirements

• Deep, hands-on experience building within a major enterprise data platform such as Palantir Foundry, including ontology modelling, pipeline development, and large-scale data platform design.
• Strong proficiency in Python, with experience applying data engineering libraries such as Spark, PySpark, Dask, or pandas to large, complex datasets.
• Familiarity with API-driven data integration across REST, GraphQL, and action-style APIs.
• Practical experience with 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, with the ability to explain data concepts to both technical and non-technical stakeholders.

Nice to have

• Experience with cloud platforms such as AWS, GCP, or Azure and infrastructure telemetry.
• Familiarity with distributed systems, monitoring data, or usage-based billing data.
• Experience supporting customer-facing data products or platforms.

Benefits and work setup

• Remote-first team with flexible working arrangements that emphasise autonomy and trust.
• Competitive compensation package including base and equity, with reviews on a roughly twelve-month cycle.
• Dynamic progression opportunities with exposure to new challenges, leadership opportunities, and ownership of impact.