About Precision AI Precision AI is on a mission to transform agriculture with cutting‑edge drone technology. Our aerial spraying systems help farmers target weeds with surgical precision, reducing chemical use and increasing yields. We’re a rapid‑moving, impact‑driven team looking for people who want to help build the future of farming. About Precision AI Precision AI is on a mission to transform agriculture with cutting‑edge drone technology. Our aerial spraying systems help farmers target weeds with surgical precision, reducing chemical use and increasing yields. We’re a fast‑moving, impact‑driven team looking for people who want to help build the future of farming. This is Precision AI's first dedicated data engineering role. Our AI team trains computer vision models on several hundred terabytes of drone imagery and flight telemetry collected across real growing seasons. Every training run depends on tribal knowledge about which prefixes hold which season. You own how field data gets from the aircraft into a governed, queryable, reproducible asset that our ML engineers can pull training sets from with confidence, and that the business can eventually report on. This is a hands‑on senior individual contributor role reporting to the AI Team Lead. You will set the technical direction for data at Precision AI and be measured on whether the AI team can move faster because of it. This hybrid role is based in Calgary and will work from Precision AI’s headquarters 3 days a week. Data Platform Architecture Pipeline Engineering and Orchestration Evaluate and select the tooling (Airflow, Dagster, Prefect, or equivalent), then build batch pipelines that are idempotent, backfillable, and observable.



Field‑to‑Cloud Ingestion Build reliable ingestion for drone imagery and telemetry captured in the field, often over poor connectivity. ML Data Enablement Work directly with AI engineers to turn raw captures into curated, versioned training and evaluation datasets. Own dataset lineage and versioning so that any model in MLflow can be traced back to the exact data that produced it, two years later. Support annotation workflows and our vector database. Data Quality and Reliability Define data contracts, automated tests, and freshness and anomaly monitoring. Establish the data quality standards the team codes against. Cost and Performance Management As the platform matures, extend it to serve business and product analytics: a modelled warehouse layer, transformation tooling such as dbt, and a semantic layer for reporting. This is a later phase of the role, not a day‑one responsibility, and you will help decide when it becomes the priority. Write production‑grade Python. Use infrastructure as code, containerization, CI/CD, and version control as defaults rather than afterthoughts. Raise the bar across the AI team through code review and by setting the data engineering standards other engineers build on. Technical Communication Communicate design decisions, tradeoffs, and progress clearly to engineers and to non‑technical partners. 4+ years building and operating production data platforms,



with clear ownership of systems you designed rather than only maintained. Strong Python, with solid software engineering fundamentals: testing, code structure, version control, and code review. Deep hands‑on AWS experience, particularly S3, Athena or equivalent query engines, IAM, and cost management at scale. Advanced SQL and demonstrable data modeling judgment. Track record of designing for reproducibility, including data versioning, lineage, and backfill correctness. Bonus Geospatial and raster data

experience:
GeoTIFF, cloud‑optimized GeoTIFF, GDAL, tiling, and coordinate reference systems. Experience supporting computer vision or ML teams, including training dataset curation, annotation pipelines, or feature stores. Familiarity with MLflow, DVC, LakeFS, or comparable experiment and data versioning tooling. Analytics engineering exposure: dbt, dimensional modelling, BI tooling. Agriculture, remote sensing, robotics, or another domain with large sensor‑derived datasets. Bachelor's or master's degree in computer science, computer engineering, software engineering, and data science. Research shows that some candidates, especially women, underrepresented groups, and career changers, are less likely to apply for a role unless they meet 100% of the listed qualifications. At Precision AI, we believe the right person can grow into the role, and we value potential as much as experience. All qualified applicants will receive consideration for employment without discrimination based on race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other status protected by law. #

📌 Senior Data Engineer (Canada)
🏢 Precision AI
📍 Canada