Build and own the facilities data pipeline for AI data center telemetry, including ingestion from industrial protocols (BACnet, Modbus, OPC UA), data quality tooling, and serving clean APIs/datasets for dashboards, controls, and ML. Requires production data pipeline experience with on-call ownership, industrial protocol integration, and full-stack debugging.
269k – 317k/yr
On-site5+ YOEData Engineering
About the role
Build the facilities data pipeline
Ingestion, transformation, and serving of telemetry from every site.
Ship the connectors that speak the field's languages: BACnet, Modbus, OPC UA, and vendor APIs, reliably.
Own data quality in code: validation, gap detection, and backfill tooling.
Serve the consumers: clean APIs and datasets for dashboards, controls, and ML.
Requirements
Shipped production data pipelines and owned them on call.
Integrated with industrial protocols or ugly vendor APIs and made them reliable.
Test data code properly: correctness, latency, and gaps.
Debug across the stack, from a flatlined sensor to a slow query.
Work fast with modern tooling and AI-assisted development.
Nice-to-Haves
Time-series systems.
Go, Python, or TypeScript.
Industrial and IoT data.
Grafana ecosystems.
Skills
Data Pipelinesbacnetmodbusopc uaindustrial protocolsvendor apisdata validationtime-series databasesGoPythonTypeScriptGrafanaiot data
Build and operate high-frequency facilities telemetry data pipelines (power, cooling, BMS, sensors) for scaling AI data centers. Stand up ingestion/streaming infrastructure, automate deployments with IaC, and own end-to-end reliability for gigawatt-scale operations.
269k – 317k/yr
On-site5+ YOEData Engineering
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