Redolent Infotech Pvt. Ltd.
Backend Engineer (Data pipeline)
Redolent Infotech Pvt. Ltd., Sunnyvale, California, United States, 94087
One of our direct clients is urgently looking for a
Backend Engineer
in
Sunnyvale, CA . TITLE:
Backend Engineer (data pipeline) LOCATION:
Sunnyvale, CA DURATION:
6 to 12+ Months Description: Job Duties: Develops innovation strategies, processes, and best practices. Drives the execution of multiple business plans and projects. Supports and ensures business objectives are met. Leads the discovery phase of medium to large projects to create a high-level design. Leads a small group of 3 to 4 engineers for assigned engineering projects. Leads and participates in end-to-end service implementations, data feeds ingestion, and orchestration. Designs and implements low latency services. Promotes and supports company policies, procedures, mission, values, and standards of ethics and integrity. Troubleshoots business and production issues. Accelerates migration of legacy on-prem Hive/Spark/Airflow pipelines and workloads to public cloud, leveraging GCP, BigQuery, and DataProc to achieve cost efficiencies and improved SLAs and data quality.
#J-18808-Ljbffr
Backend Engineer
in
Sunnyvale, CA . TITLE:
Backend Engineer (data pipeline) LOCATION:
Sunnyvale, CA DURATION:
6 to 12+ Months Description: Job Duties: Develops innovation strategies, processes, and best practices. Drives the execution of multiple business plans and projects. Supports and ensures business objectives are met. Leads the discovery phase of medium to large projects to create a high-level design. Leads a small group of 3 to 4 engineers for assigned engineering projects. Leads and participates in end-to-end service implementations, data feeds ingestion, and orchestration. Designs and implements low latency services. Promotes and supports company policies, procedures, mission, values, and standards of ethics and integrity. Troubleshoots business and production issues. Accelerates migration of legacy on-prem Hive/Spark/Airflow pipelines and workloads to public cloud, leveraging GCP, BigQuery, and DataProc to achieve cost efficiencies and improved SLAs and data quality.
#J-18808-Ljbffr