Senior Data Engineer

Snowcap is approaching its October 2026 proof point campaign with a cryogenic test facility in the Jacksonville metro that will generate more telemetry than any existing system in our stack can handle. The data infrastructure needs to be built before the first wafers go on the prober. This role owns that build.

You will own our Manufacturing Intelligence platform end-to-end — from raw telemetry ingestion through the analytics layer that the physics and process teams act on. You will work directly with test engineers, process scientists, and fab leads in a flat, matrixed organization where every domain lead has a direct line to your work. There are no layers between you and the problem.

In twelve months, the right person in this role will have a production-grade pipeline processing telemetry from cryogenic test runs, process tools, and environmental sensors — with the observability and data quality guarantees the team needs to trust it. That infrastructure becomes the foundation on which Snowcap's path to volume manufacturing is built.

• Own our Manufacturing Intelligence platform end-to-end — architecture, implementation, and evolution — from raw equipment telemetry through analytics delivery to the engineers who act on it.
• Design and build the ETL pipeline infrastructure connecting our cryogenic test systems, 300mm wafer prober, process tools, and environmental sensors to a centralized data lake, with strong guarantees on integrity and lineage.
• Define the data schema, quality standards, and wafer-to-device traceability model that will govern Snowcap's manufacturing data across all future process nodes and test campaigns.
• Lead onboarding of new high-volume data sources as our test facility scales — including equipment telemetry interfaces and custom instrument integrations — without degrading pipeline reliability.
• Build the observability and monitoring layer that ensures every engineer in the building trusts the data they act on: alerting, lineage tracking, and anomaly detection across the full pipeline.
• Partner directly with physics, test, and process integration leads to translate manufacturing requirements into data architecture decisions — and push back when the requirements don't justify the complexity they introduce.
• Establish the data engineering standards, tooling, and documentation that define how every subsequent hire in this function builds.

• 8+ years building production-grade data pipelines at scale — not proofs of concept, but systems that ran in production and had your name on them when something went wrong.
• Deep hands-on experience with large-scale telemetry ingestion: streaming, batch, real-time, and the architectural trade-offs between them in high-throughput manufacturing or instrumentation environments.
• Strong proficiency in Python and SQL; fluency with at least one large-scale distributed data processing framework.
• Experience in semiconductor fab, capital equipment, or test environments where data has physical meaning and errors have material consequences for the product.
• Demonstrated ability to design for observability and data quality from the start — not bolted on after the fact.
• BS or MS in Computer Science, Computer Engineering, Electrical Engineering, or equivalent demonstrated depth through industry work.

• Hands-on experience with SECS/GEM, EDA/Interface A, or other semiconductor equipment telemetry protocols — this is the interface layer our data originates from.
• Experience designing and deploying data infrastructure on a major cloud platform, including event streaming, object storage, and managed database services.
• Background in semiconductor yield analysis, statistical process control, or equipment health monitoring pipelines where the output directly informs manufacturing decisions.
• Exposure to cryogenic systems, superconducting electronics, or other physics-intensive manufacturing environments where measurement precision is non-negotiable.
• Open-source contributions, patents, or shipped tooling that reflects ownership beyond the scope of a job description.
• Demonstrated ability to prototype complex data architecture ideas quickly and communicate their value to non-data-engineering audiences.

Snowcap Compute is building the world's first commercial superconducting digital computer. Our chips operate at 4.5 Kelvin on Josephson junction-based SFQ and AQFP logic, consuming a fraction of the power of any CMOS system. We are not improving the existing paradigm — we are replacing it.

We are a technically elite, small team at the intersection of quantum physics, cryogenic engineering, and advanced semiconductor manufacturing. Every person on this team has a direct line of sight to the product. The work you do here will be visible, consequential, and unprecedented.

This is the founding data engineering hire at Snowcap. The architecture you choose and the standards you set will define how every subsequent member of this function builds. That is a rare kind of ownership.

Full medical, dental, and vision · 401(k) · Relocation assistance available for candidates outside the area.

Based in Jacksonville Metro, FL · On-Site.