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Data Engineer

TechBiz Global GmbH
CompanyTechBiz Global GmbH
CategoryEngineering
LocationPondicherry
RemoteRemote
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted
Last verified30 Jul 2026
SourceEmployer career page (recruitee)
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Description
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. Key Responsibilities: ● 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 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 data requirements. ● 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 Iceberg. Required Skills and Qualifications: ● 5+ years of hands-on experience as a Data Engineer or similar role. ● Strong experience with Apache Kafka and Kafka Connect (sink and source 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
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