Data Pipeline AgTech Engineer
Description • Architect and scale the data backbone that powers every leaf, sensor, and harvest across Sensei Ag’s global network of high-tech greenhouses. You will own the end-to-end design, deployment, and evolution of real-time data pipelines that ingest millions of IoT readings per hour—from humidity and CO₂ sensors to computer-vision cameras and robotic harvesters—turning raw telemetry into actionable agronomic intelligence. • Build fault-tolerant, low-latency ingestion layers using Kafka, Kinesis, or Pulsar that stream directly into a cloud-native lakehouse (Snowflake, BigQuery, or Databricks). You will define partitioning strategies, retention policies, and exactly-once semantics so that every gram of produce can be traced back to the precise environmental conditions that created it. • Develop modular transformation workflows in Python, Scala, or SQL that cleanse, enrich, and join disparate data sets: greenhouse telemetry, ERP seed-to-sale records, satellite weather feeds, and genomic crop profiles. Your pipelines will power dashboards that let plant scientists ask, “Why did this cultivar thrive in Nevada but stall in Hawai‘i?”—and get an answer in seconds. • Collaborate daily with plant biologists, computer-vision engineers, and farm-operations teams to translate agronomic questions into data contracts. You will whiteboard schema designs that balance analytical flexibility with storage cost, then pair-program ML feature stores that enable predictive models for yield, pest pressure, and harvest timing. • Champion data governance and privacy from day one. You will implement GDPR-compliant anonymization, role-based access controls, and automated PII detection so that as Sensei Ag scales across continents, trust in the data scales with it. • Automate CI/CD for data infrastructure using Terraform, Helm, and GitHub Actions. Every pull request triggers integration tests that spin up ephemeral clusters seeded with synthetic greenhouse data, ensuring that a schema change in Tokyo never breaks a harvest forecast in California. • Optimize for sustainability as aggressively as for speed. You will benchmark pipeline energy consumption, choose columnar formats that cut storage by 40 %, and schedule batch jobs to run when the local grid is greenest—because at Sensei Ag, every byte should help the planet, not hurt it. • Establish SLAs that the business can bet on: <5-minute lag from sensor to dashboard, 99.9 % pipeline uptime, and recoverability within 15 minutes of regional outages. You will build self-healing jobs that page you only when human judgment is truly required. • Mentor junior engineers and data analysts through code reviews, lunch-and-learns, and pair debugging sessions. Your documentation will become the canonical reference that turns greenhouse operators into confident SQL authors. • Stay on the leading edge of AgTech data trends—NDVI imagery at 10 cm resolution, hyperspectral cameras that detect disease before the human eye, edge AI on greenhouse micro-controllers—and prototype integrations that keep Sensei Ag two harvests ahead of the competition. Apply tot his job