Key skills
Google Cloud Platform (GCP)BigQueryData Warehouse ArchitectureData ModelingPerformance OptimizationETL/ELT PipelinesDataflowDataprocCloud ComposerTeradataPythonData QualityData SecurityData GovernanceCI/CDTestingMonitoringETLELTData EngineeringAnalyticsGCPcomposerLeadership
About this role
Capgemini is a leading provider of Staffing and Consulting Services, and they are seeking a Data Engineering Lead specializing in GCP BigQuery. The role involves leading the design and delivery of enterprise-scale data platforms, ensuring data quality and reliability, and providing technical leadership across data engineering teams.
Responsibilities:
- Lead the design and delivery of enterprise-scale data platforms on GCP, with BigQuery as the core analytics engine
- Own data warehouse architecture, data modeling, and performance optimization in BigQuery
- Design and implement ETL/ELT pipelines using GCP services like Dataflow, Dataproc, and Cloud Composer
- Provide technical leadership for Teradata environments and drive migration or coexistence strategies with GCP
- Ensure data quality, reliability, security, and governance across all data pipelines
- Optimize systems for scalability, cost efficiency, and operational excellence
- Build and enforce engineering best practices including CI/CD, testing, and monitoring
- Lead, mentor, and review the work of data engineers and senior engineers
- Collaborate with analytics, data science, and business stakeholders to translate requirements into solutions. Able to work in Python
- Act as a trusted technical advisor for modern data architecture and platform strategy
Requirements:
- Lead the design and delivery of enterprise-scale data platforms on GCP, with BigQuery as the core analytics engine
- Own data warehouse architecture, data modeling, and performance optimization in BigQuery
- Design and implement ETL/ELT pipelines using GCP services like Dataflow, Dataproc, and Cloud Composer
- Provide technical leadership for Teradata environments and drive migration or coexistence strategies with GCP
- Ensure data quality, reliability, security, and governance across all data pipelines
- Optimize systems for scalability, cost efficiency, and operational excellence
- Build and enforce engineering best practices including CI/CD, testing, and monitoring
- Lead, mentor, and review the work of data engineers and senior engineers
- Collaborate with analytics, data science, and business stakeholders to translate requirements into solutions
- Able to work in Python
- Act as a trusted technical advisor for modern data architecture and platform strategy