Data Engineer

Responsibilities:

• Design, deploy, and operate industrial data lake platforms.
• Build and maintain end-to-end data pipelines.
• Implement data bridges for sensor connectivity and industrial data integration.
• Optimize data infrastructure in collaboration with DevOps and application teams.
• Ensure data quality, validation, governance, and reliable data processing.
• Support industrial IoT, machine data, MES, and Manufacturing 4.0 use cases.

Requirements:

• Experience: 7-10 Years
• Strong data engineering and end-to-end data pipeline experience.
• Hands-on experience with Apache Camel K and PySpark.
• Experience designing, deploying, and operating Data Lake/Lakehouse architecture.
• Strong SQL and database knowledge, including PostgreSQL, InfluxDB, or other time-series databases.
• Experience with Kafka, Redpanda, MQTT, or similar messaging technologies.
• Knowledge of data ingestion, transformation, validation, data quality, and governance.
• Experience implementing IoT/IIoT sensor connectivity and data integration.

Good to Have:

• Apache Iceberg, MinIO, Trino, EMQX, and MQTT-to-Kafka integration.
• Data lineage/cataloguing tools such as OpenMetadata or Collibra.
• Docker, Kubernetes, CI/CD, and DevOps practices.
• Advanced analytics tools such as Qdrant, Neo4j, or Braincube.