Data Architect - IoT Azure

Responsibilities:

• Lead the design and delivery of end-to-end industrial IoT architecture spanning edge to cloud, enabling real-time data ingestion, time-series analytics, and cloud-scale AI/analytics for manufacturing and industrial operations.
• Drive platform strategy, standardisation, and scalability of IIoT solutions aligned with enterprise data and AI platforms.
• Define and implement edge-cloud IIoT architecture (edge devices, streaming, cloud analytics, AI).
• Design real-time data pipelines for high-frequency industrial/time-series data.
• Architect integration of OT systems (PLC, SCADA, MES) with cloud data lakehouse platforms.
• Lead cloud analytics strategy (streaming, batch, AI/ML on industrial data).
• Establish IIoT platform standards (data models, ingestion patterns, governance, security).
• Design observability solutions using Grafana + time-series platforms.
• Ensure scalability, resilience, and security of IoT ecosystems.
• Partner with data, AI, and business teams to enable use cases: Predictive maintenance, asset performance monitoring, quality analytics, energy optimisation.

Success Metrics:

• Scalable, standardised edge-to-cloud IIoT platform.
• Reliable real-time ingestion and processing of industrial data.
• Enterprise adoption of cloud analytics on IIoT data.
• Measurable impact on uptime, efficiency, and operational visibility.

Requirements:

• Bachelor's/Master's in engineering, computer science, or a related field.
• 8-12+ years in IoT/IIoT, data platforms, or industrial automation.
• Proven experience designing real-time, cloud-scale IIoT architectures.
• Required Skills: Architecture and Platforms.
• Edge-Cloud Architecture: gateways, edge computing, cloud ingestion, and analytics layers.
• GCP Analytics Stack: Pub/Sub, Dataflow, BigQuery, Bigtable, Vertex AI.
• Time-Series Platforms: InfluxDB, TimescaleDB, Bigtable.
• Streaming and Real-Time Analytics: Kafka / Pub/Sub, event-driven architectures.
• Visualisation: Grafana.

Industrial and Integration:

• OT systems: PLC, SCADA, MES.
• Protocols: OPC-UA, MQTT, Modbus.
• Experience integrating factory systems with enterprise data platforms.

Data and AI:

• Real-time analytics, data pipelines, and lakehouse architectures.
• ML/AI use cases on industrial data (predictive maintenance, anomaly detection).

Security and Networking:

• IoT security (device identity, encryption, zero-trust principles).
• Industrial network architectures.