Lead Data Engineer

Edge Data Acquisition & Protocol Integration (Primary Focus)

• Design and implement robust edge data ingestion pipelines to acquire telemetry and control data from all equipment’s and systems using BACnet (IP/Serial), Modbus, and data‑center‑specific protocols.

• Develop edge agents capable of running in air-gapped, on-prem-only environments

• Implement data streams integration from controls platforms, field devices, and supervisory systems

• Ensure ingestion layer meets KPIs: Low latency, High throughput, Fault tolerance

Embedded Data Storage & Pre‑Processing

• Design and implement local edge storage solutions, including hot and cold data tiers, optimized for performance, retention, and reliability.

• Implement data preprocessing, filtering, aggregation, feature extraction, and quality checks at the edge to support downstream analytics and models.

Platform Architecture & Orchestration

• Design a layered edge platform architecture that cleanly separates data ingestion, storage, preprocessing, analytics, and application layers. Build and manage containerized workloads (e.g., Docker‑based) and orchestration at the edge.

• Implement mechanisms to schedule, invoke, and manage digital twin executions

Analytics, Digital Twin & Controls Integration

• Enable analytics algorithms and digital twins to consume edge data through well‑defined APIs and data services.

• Support deployment and optimization of AI/ML algorithms on edge hardware

Basic Qualifications

• Bachelor’s or Master’s degree in Computer Engineering, Electrical Engineering, or a related field.

• 6+ years of experience in embedded systems, edge platforms, or data/analytics engineering.

Preferred Qualifications

• Strong experience designing edge data platforms, including ingestion, storage, and analytics pipelines.

• Hands‑on expertise with industrial and building automation protocols (BACnet, Modbus, and related field protocols).

• Familiarity with: Edge orchestration frameworks (K3s, Balena, Azure IoT Edge, AWS Greengrass), Time-series databases (InfluxDB, Timescale, Prometheus, QuestDB), Digital twin data models or graph schemas

• Background in secure-by-design industrial deployments

• Experience with real-time event stream processing

• Prior experience implementing data validation, anomaly detection, and QC at the edge

• Experience with hot/cold data storage architectures, time‑series databases, and embedded data persistence.

• Solid understanding of semantic data models and ontologies, such as Brick Schema and Project Haystack.

• Proficiency in Python, C/C++, and JavaScript, with experience building web‑based configuration or visualization interfaces.

• Experience with containerization and orchestration for edge deployments.

• Familiarity with controls systems integration and real‑time data constraints.

• Experience integrating AI/ML workloads at the edge, including performance tuning and accelerator (NPU) usage.

  • Familiarity with Carrier ecosystems (WebCTRL, PIC, Nlyte, chillers/CDUs/CRAHs)