Backend Data Engineer

Description:

Backend Data Engineer

PLANET.TECH — Austin, TX (On-Site)

About PLANET.TECH

PLANET.TECH builds hardware that solves planetary-scale problems, starting with agriculture. We have two products on a shared electronics and sensor platform. The first is a real-time biological monitoring system for cattle — non-invasive biometric sensors that detect illness hours before visual symptoms appear, turning individual-animal health data into actionable intelligence for farmers and veterinarians. The second, Moola, is a halter-mounted wearable that captures enteric methane emissions from cattle and converts them to renewable natural gas and sustainable aviation fuel.

Both products generate continuous, high-volume sensor data from connected devices on live animals. The data infrastructure underneath them — ingestion, storage, processing, and delivery — is what makes the whole thing work. Right now, we need the person who builds it.

We're a small team in Austin, TX, mid-raise, deploying our first devices this summer.

What you'll build

1. Data infrastructure — this is day one.

Ingestion that can absorb continuous, high-volume sensor data from connected devices in the field without choking. Schema and storage design for time-series data. The pipelines (streaming + batch) that feed monitoring, analytics, and downstream ML. A path from our working prototype data layer to production architecture — you own the technology choices, and you build it so it extends cleanly as our device fleet scales from hundreds to tens of thousands.

2. Data visibility and dashboards.

A functional interface where live device data is visible — real-time readings, historical trends, alert states. This is how farmers, partners, and investors see the platform working. It doesn't need to be beautiful on day one. It needs to work, update in near-real-time, and be something we can point to within weeks of your start date.

3. API and integration layer.

RESTful APIs that serve the dashboard, future mobile apps, and third-party integrations. Clean, documented, built to extend.

4. Foundation for ML and intelligence.

You're not building the detection algorithms — we have veterinary scientists and domain experts for that. But your data layer is what they build on. Design it so a data scientist can query months of multi-sensor time-series data across thousands of animals without writing custom ETL every time.

You're probably a fit if you

• Have 3-7 years building backend systems, at least some of it at a company that shipped an IoT, telemetry, or connected-device product — fleet management, smart agriculture, industrial sensors, connected medical devices, or similar
• Have personally built a data ingestion pipeline from scratch — not just maintained one
• Are strongest in databases and data infrastructure: comfortable with SQL, time-series storage tradeoffs, and the gap between "it works locally" and "it works on a ranch in South Texas with one bar of cell signal"
• Have deployed production workloads on AWS or GCP — not just run tutorials
• Can stand up a working dashboard that displays live data reliably
• Ship fast, stay calm in ambiguity, and don't need a product manager to define every ticket
• Want to be in the room. This is an on-site, build-together role in Austin.

Bonus (genuinely useful, not checkbox filler):

• Experience with agricultural, environmental, or biological sensor data
• Edge computing or gateway architecture — data from remote locations with unreliable connectivity
• IoT device communication protocols and fleet management
• Basic ML pipeline awareness — feature stores, training data management, model serving
• Experience scaling from prototype to thousands of devices

What you're not:

• An architect who wants to whiteboard for three months before writing code
• Someone who needs a large team and established processes to be productive
• Someone uncomfortable building in the open with real hardware generating real data