Architects and manages scalable data ingestion pipelines connecting SaaS, cloud, on-premises, non-SAP, and Oracle data sources. Administers Fivetran, establishes secure connectivity, implements CDC and incremental replication, monitors pipeline performance, troubleshoots issues, and optimizes data transfer reliability and synchronization.
Summary
We are seeking an experienced Senior Data Ingestion Engineer to architect and manage robust data transfer solutions within our IT Services division. This pivotal role focuses on integrating diverse data sources, including SaaS platforms, cloud environments, and on-premises systems, to ensure seamless and reliable data flow. The ideal candidate will leverage expertise with Fivetran and advanced replication techniques to support non-SAP ecosystems and Oracle databases, driving efficiency and data integrity across the organization.
Responsibilities
- Design and maintain scalable data pipelines connecting SaaS and cloud-based data sources.
- Establish and manage secure connectivity protocols for on-premises infrastructure.
- Continuously monitor data ingestion workflows to identify bottlenecks and implement performance optimizations.
- Configure and administer Fivetran tools to facilitate smooth data transfers.
- Develop strategies for ingesting data from non-SAP applications and various relational database systems.
- Implement Change Data Capture (CDC) and incremental replication mechanisms to ensure real-time data synchronization.
Requirements
Requirements:
- Minimum of six years of professional experience in data engineering or related IT services roles.
- Proven proficiency with Fivetran and other leading data integration platforms.
- Strong background in handling SaaS and cloud data source integrations.
- Expertise in building on-premises connectivity solutions.
- Deep understanding of Oracle and general relational database architectures.
- Hands-on experience with CDC methodologies and incremental data replication strategies.
- Ability to monitor, troubleshoot, and optimize complex data pipelines.
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