Puedes aplicar desde República Dominicana
Data Engineer
Worldwide · Remoto
Resumen de la oportunidad
Data Engineer en TechBiz Global. Es una oportunidad remota. Ubicación publicada: Worldwide. La fuente aporta evidencia de que puede solicitarse desde República Dominicana.
- Empresa
- TechBiz Global
- Ubicación
- Worldwide
- Modalidad
- Remoto
- Tipo de contrato
- FULL TIME
- Disponibilidad desde República Dominicana
- Puedes aplicar desde República Dominicana
- Actualizada
- 01 de octubre de 2026
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Descripción del empleo
Sobre el empleo
At TechBiz Global , we are providing recruitment service to our TOP clients from our portfolio. We are currently seeking an Data Engineer to join one of our clients ' teams. If you're looking for an exciting opportunity to grow in a innovative environment, this could be the perfect fit for you.
Responsabilidades
- Design, develop, and maintain data ingestion pipelines using Kafka Connect and
- Debezium for real-time and batch data integration.
- Ingest data from MySQL and PostgreSQL databases into AWS S3, Google Cloud
Requisitos
- Optimize data ingestion processes for performance and cost efficiency.
- Contribute to automation and deployment scripts using Python and cloud-native tools.
- Stay updated with emerging data lake technologies such as Apache Hudi or Apache
- 5+ years of hands-on experience as a Data Engineer or similar role.
- Strong experience with Apache Kafka and Kafka Connect (sink and source
Storage (GCS), and BigQuery.
- Implement best practices for data modeling, schema evolution, and efficient partitioning
in the Bronze Layer.
- Ensure reliability, scalability, and monitoring of Kafka Connect clusters and connectors.
- Collaborate with cross-functional teams to understand source systems and downstream
connectors).
- Experience with Debezium for change data capture (CDC) from RDBMS.
- Proficiency in working with MySQL and PostgreSQL.
- Hands-on experience with AWS S3, GCP BigQuery, and GCS.
- Proficiency in Python for automation, data handling, and scripting.
- Understanding of data lake architectures and ingestion patterns.
- Solid understanding of ETL/ELT pipelines, data quality, and observability practices.
Good to Have:
- Experience with containerization (Docker, Kubernetes).
- Familiarity with workflow orchestration tools (Airflow, Dagster, etc.).
- Exposure to infrastructure-as-code tools (Terraform, CloudFormation).
- Familiarity with data versioning and table
Originally posted on Himalayas