Data Operations Specialist
Halter
| Company | Halter |
| Category | Operations & Admin |
| Location | Auckland |
| Remote | Hybrid |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 30 Jul 2026 |
| Last verified | 3 Aug 2026 |
| Source | Employer career page (ashby) |
Description
ABOUT HALTER
At Halter, we’re on a mission to enable farmers and graziers to run the most productive and sustainable operations. Our customers are using Halter to break free from the time-intensive constraints of conventional practices. Imagine watching 500 cattle stand up and walk calmly towards their next break? No quad bikes, no dogs, no fences. Just a group of cattle walking at their own pace. People say it looks like magic. Our customers are revolutionizing grazing with Halter. It's changing lives and transforming an industry. People join Halter to do meaningful work. By joining us you’ll be solving challenging problems within a talented team and a culture built for high performance. Our team out-think, out-work and out-care. We’re committed to delivering real change in the world - this isn’t easy, and in truth, we love that it’s hard.
We’re backed to deliver on a mission that matters by Tier 1 investors including Founders Fund https://foundersfund.com, Bessemer Venture Partners https://www.bvp.com/, BOND, https://www.bondcap.com/ DCVC https://www.dcvc.com/, Blackbird https://www.blackbird.vc/, Promus Ventures https://www.promusventures.com/, Rocket Lab’s Peter Beck and Icehouse ventures https://www.icehouseventures.co.nz/?utm_term=icehouse%20ventures&utm_campaign=Brand+Keywords&utm_source=adwords&utm_medium=ppc&hsa_acc=4064636360&hsa_cam=16902737884&hsa_grp=134004309485&hsa_ad=592971340272&hsa_src=g&hsa_tgt=kwd-1082530126230&hsa_kw=icehouse%20ventures&hsa_mt=b&hsa_net=adwords&hsa_ver=3&gclid=Cj0KCQjw2v-gBhC1ARIsAOQdKY0J0DepRVFDmjQlAkJPbZuQnLSA5UVzUzOXYiPxkjX-SeVa513_BhkaAr-MEALw_wcB.
To find out more, visit our LinkedIn https://www.linkedin.com/company/halter-limited & Instagram https://www.instagram.com/lifeathalter/.
Term: 6 months with the possibility of progressing to full-time if performance and business need allow.
Data is in our DNA at Halter, and represents one of our highest leverage assets for delivering value to farmers and ranchers. The performance of Halter’s predictive models are hugely influenced by the ground truth data used to train them. As such, we’re building a dedicated machine learning Data Operations function to own the full lifecycle of ground truth datasets: from collection of measurements with wide geographic diversity, through to annotation, and quality control—so that our ML teams can innovate with urgency, and ship reliable, high‑impact capabilities to farmers.
WHAT YOU’LL DO
You will be on the ground collecting the real-world measurements and field data that our machine learning models depend on. This is hands-on, high-volume work where quality and consistency matter as much as speed. You'll execute the collection campaigns that feed our ground-truth datasets — plate-metering paddocks, monitoring cow behaviour, capturing drone imagery — and be the front line that makes sure the data reflects what's actually happening in the paddock.
IN-FIELD DATA AND MEASUREMENT COLLECTION
- Coordinate and interact with farmers to respectfully engage with their farms for the purpose of collecting data to build product to serve them
- Carry out ground-truth data collection on farms: pasture biomass measurements, sensor readings, imagery capture, and animal or environmental observations against defined protocols to name a few.
- Operate collection equipment and tooling correctly and safely in variable field conditions (weather, terrain, livestock), and keep gear maintained, calibrated, and campaign-ready.
- Follow sampling plans precisely — right sites, right cadence, right method — so datasets are representative and comparable across time and location.
- Capture accurate metadata at the point of collection (location, timestamp, conditions, device, method) so every measurement is traceable back to reality.
DATA QUALITY AT THE SOURCE
- Apply annotation and labelling guidelines consistently, flag ambiguous or edge cases, and escalate rather than guess.
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