About the role
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.