Senior Analytics Engineer

Nscale•Seattle, WA

8d•$100,000 - $150,000•Hybrid

About The Position

We're hiring a Senior Analytics Engineer to own the analytics layer that powers data-driven decisions across Nscale's GPU procurement and hardware supply chain. In this role, you'll sit within the Supply Chain Analytics & Automation team and build in Palantir Foundry, creating the output datasets, data models, and reporting products that teams use directly. You'll work closely with Supply Chain, Finance, Capacity stakeholders, the C-suite, and data engineering, serving as the link between business decision-making and the underlying data required to support it. This is a senior individual contributor role and the first analytics hire on the team, giving you the opportunity to set standards for quality, documentation, and architectural decision-making from the ground up. Your work will shape how critical supply chain decisions are understood, trusted, and acted on as Nscale continues to scale.

Requirements

5+ years of experience in an analytics engineering or senior analytics role, including work in at least one production environment at meaningful scale
Strong experience with SQL, data modelling, and data transformation
Palantir Foundry experience strongly preferred, with comparable platform experience also considered
Demonstrated ability to build analytical outputs, datasets, models, and reporting layers used by business stakeholders
Strong instinct for data quality and a high bar for reconciliation and accuracy
Experience working across technical and non-technical stakeholders
Ability to write a clear data spec for engineers and a concise summary for C-suite audiences
Track record of making strong architectural decisions in fast-moving environments with incomplete requirements

Nice To Haves

Background in supply chain, procurement, hardware, or AI/cloud infrastructure is a meaningful bonus

Responsibilities

Own the analytics layer
Build output datasets, data models, and reporting products in Palantir Foundry.
Design analytical outputs that business stakeholders can use directly.
Maintain the reporting layer that supports decision-making across the organization.
Partner across technical and business teams
Serve as the primary interface between the analytics team and data engineering.
Assess what data exists, what shape it is in, and what needs to be built or changed to answer business questions.
Partner with Supply Chain, Finance, and Capacity stakeholders to translate decisions into analytical requirements.
Communicate effectively with both technical and non-technical audiences, including senior stakeholders.
Set quality and operating standards
Define engineering standards for analytical outputs.
Establish documentation standards, including assumption documentation and version control.
Implement reconciliation checks to ensure analytical outputs are accurate and trusted.
Raise the quality bar for how analytics work is produced and maintained.
Shape the future of the function
Make architectural decisions that shape the analytics layer as the team grows.
Set expectations for what strong analytical work looks like in a fast-moving environment.
Hire and onboard analysts over time, helping define the foundation for future team members.