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.

 

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.

  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 moden 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.