Data Engineer with Fivetran

Capgemini Engineering is a global leader in engineering services, bringing together engineers, scientists, and architects to provide R&D and engineering services across industries including autonomous cars and robotics. Capgemini is a global business transformation partner specializing in AI, cloud, and data with over 410,000 team members worldwide.

• 4+ years of experience in Data Engineering, Data Integration, ETL/ELT Development, or Data Platform Engineering
• Hands-on expertise in Fivetran HVR administration, configuration, and troubleshooting
• Strong understanding of Change Data Capture (CDC) and enterprise replication patterns
• Experience with large-scale relational databases including SQL Server, PostgreSQL, and/or SAP HANA
• Proficiency in advanced SQL and Python scripting
• Experience building and supporting cloud-based data platforms on Azure and Databricks or equivalent
• Ability to manage enterprise-scale production data environments with focus on reliability and data quality

Preferred:

• Familiarity with DataOps, CI/CD, and infrastructure automation
• Experience with data modelling, data warehousing, and dimensional design
• Familiarity with SAP source systems and enterprise application integrations
• Experience working in Agile delivery environments

• Designing, developing, and maintaining data integration and replication solutions using Fivetran HVR with CDC-based pipelines across hybrid and cloud environments
• Optimizing replication performance including throughput, latency, and data availability
• Monitoring and resolving pipeline failures and data quality issues
• Collaborating with Data Architects, DBAs, Cloud Engineers, Application Teams, and Business Analysts on data integration solutions
• Supporting enterprise cloud data platform initiatives including migration to Azure Data Lake and Databricks
• Performing root cause analysis on data integration incidents and implementing corrective actions
• Developing automation and data processing scripts using Python and SQL
• Contributing to DataOps practices, CI/CD processes, and data quality frameworks