Data Engineer – Industrial IoT & Real-Time Analytics

We are looking for a Data Engineer to design and implement large-scale, real-time data architectures that power AI, predictive maintenance, and industrial automation in mission-critical environments. This role will ensure high availability, accuracy, and efficiency in processing real-time industrial data.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.
• 5+ years of experience in real-time data engineering, cloud computing, or industrial IoT.
• Strong expertise in data streaming, event-driven processing, and distributed computing frameworks.
• Hands-on experience with best practices with Big Data using Kafka, Apache Flink, Spark Streaming, or similar technologies.
• Deep knowledge of ETL design, data warehousing, and real-time analytics.
• Experience with SQL and NoSQL databases, including TimescaleDB, InfluxDB, Cassandra, or MongoDB.
• Proficiency in Python, Scala, or Java for data processing and automation.
• Understanding of IoT protocols (MQTT, OPC-UA, Modbus) and industrial data standards.
• Familiarity with DevOps and MLOps best practices, including CI/CD pipelines for data workflows.
• Knowledge of cloud-native data solutions (AWS Kinesis, Google Pub/Sub, Azure Event Hub).
• Familiarity with workflow orchestration platforms like Apache Airflow.

Preferred Qualifications:

• Experience in mining, oil & gas, or large-scale industrial automation projects.
• Knowledge of machine learning model deployment in real-time production environments.
• Understanding of GIS data processing, geospatial analytics, and digital twin integrations.
• Experience with cybersecurity frameworks for industrial data environments.

Soft Skills:

• Strong problem-solving mindset, capable of optimizing large-scale data architectures.
• Ability to collaborate across teams, including AI, cloud, and industrial operations.
• Effective communication skills for translating technical data insights into actionable business strategies.
• Passion for real-time data processing, AI-driven automation, and industrial innovation.

Responsibilities:

• Design and implement real-time data pipelines for ingesting, processing, and visualizing industrial sensor data.
• Develop low-latency, high-throughput streaming architectures using Kafka, Apache Flink, or Spark Streaming.
• Build and optimize secure, scalable data lakes and ETL pipelines for mining, oil & gas, and heavy industry applications.
• Implement event-driven architectures and data transformation workflows for AI-powered automation.
• Ensure data governance, security, and compliance with industrial cybersecurity standards (IEC 62443, ISO 27001, GDPR).
• Work with AI engineers, cloud architects, and automation teams to enable real-time AI decision-making.
• Optimize time-series data processing using TimescaleDB, InfluxDB, or OpenTSDB.
• Deploy cloud-based data solutions with AWS Kinesis, Google Pub/Sub, Snowflake, or Grafana.
• Monitor data pipeline performance with modern observability frameworks (e.g. Prometheus), ensuring fault tolerance, scalability, versioning, and efficiency.
• Collaborate with industrial automation and IoT teams to integrate edge-to-cloud data flows.