Data Engineer

We need someone who's spent real time in backend systems at scale, not just data pipelines in isolation, but the kind of engineer who understands what happens when systems have to hold up under real load and real consequences. We're past the point where "good enough" data infrastructure works. As more of Analog's products go live and start generating real-time data (from deployed robots, live events, athlete performance, spatial sensors), we need someone senior enough to build a platform that can take that on without falling over.

Designing and building the core data infrastructure that feeds Analog's products: ingestion, transformation, storage, and the pipelines connecting all of it

Making real architectural calls on how we handle data at scale, not just executing someone else's spec

Building for reliability and performance from day one, since this platform will sit underneath products already live with real users, governments, and enterprise partners

Working directly with the engineers building Hive, RaaS, and our other product lines to understand what they actually need from the data layer, and building it with them, not for them

There's no legacy system to maintain here and no ten-layer org chart to get a decision through. Engineering at Analog is flat: no tech leads, no middle management, just you and the other engineers building this. If you see a better way to structure the platform, you build it, you don't pitch it up a chain and wait. And unlike most data engineering roles, the data you're working with isn't abstract, it's coming off physical robots on-site, live venues, and athletes in real time.

Deep backend engineering experience in high-scale, production systems, not just analytics or reporting pipelines

Strong hands-on experience with distributed data processing (e.g. Spark) and workflow orchestration (e.g. Airflow)

Real experience designing data architecture from scratch: schema design, storage tradeoffs, ingestion at scale, not just working within an existing setup

Solid grounding in cloud data infrastructure (AWS or GCP) and the operational side of running it (cost, performance, reliability)

Comfortable owning ambiguity: this is an early platform, not a mature one with a playbook already written

Nice to have: experience with modern lakehouse tooling (dbt, Iceberg or similar), or exposure to real-time/streaming data specifically.

This isn't a fit for someone who wants a fully mapped-out roadmap handed to them, or who needs layers of process and sign-off to move. If you want to build data infrastructure for physical, real-world systems and make real calls on how it's built, this is that. If you'd rather maintain something that already exists, this probably isn't it.