We are looking for an experienced SCADA & Data Architect to design and implement industrial data architectures that integrate SCADA (Ignition), IoT, Unified Namespace (UNS), and cloud data platforms. The ideal candidate will have hands-on experience with Ignition SCADA, HiveMQ (UNS), Azure Data Factory, and Snowflake, ensuring seamless data flow, scalability, and real-time analytics across industrial systems.

Key Responsibilities:

• Design, develop, and optimize SCADA (Ignition) and IoT data architectures for industrial automation.
• Implement Unified Namespace (UNS) using HiveMQ to enable real-time data streaming and interoperability.
• Integrate SCADA and IoT data pipelines with Azure Data Factory and Snowflake for efficient storage, processing, and analytics.
• Develop robust data ingestion, transformation, and processing workflows for industrial systems.
• Ensure high availability, security, and scalability of industrial data platforms.
• Work with MQTT, OPC UA, and other industrial communication protocols for seamless data exchange.
• Collaborate with cross-functional teams to optimize industrial data workflows and analytics.
• Provide technical leadership and best practices for SCADA, IoT, and cloud data integration.

Essential Skills & Qualifications:

• Strong experience with Ignition SCADA for industrial automation and control systems.
• Hands-on expertise with HiveMQ and Unified Namespace (UNS) for IoT and data streaming.
• Experience in designing and implementing Azure Data Factory workflows.
• Proficiency in Snowflake for industrial data storage, ETL, and analytics.
• Knowledge of MQTT, OPC UA, and industrial IoT protocols.
• Experience with real-time data processing and event-driven architectures.
• Strong understanding of industrial data modeling, pipelines, and cloud integration.
• Proficiency in Python, SQL, or scripting languages for data processing.

Preferred Qualifications:

• Experience with edge computing and IoT gateways.
• Knowledge of cybersecurity best practices for industrial data architectures.
• Familiarity with DevOps/MLOps practices for data pipeline automation.