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# GCP Data Engineer

- **Company:** Capgemini
- **Location:** Temple, TX, United States
- **Experience:** Experienced
- **Salary:** $100,963.0 - $157,768.0
- **Contract:** Temporary contract
- **Skills:** Automation of Tests, Microsoft Azure, Big Data, Continuous Integration, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Warehousing, Database Design, Distributed Computing Environment, Python (Programming Language), Performance Tuning, Cloud Services, DataOps, SQL Databases, Workflow Management Systems, Cloud Platform System, Data Lakes, Infrastructure Automation Frameworks, Data Pipelines
- **Published:** October 5, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5dc26cf1d5b0382a

## About the Role

* 3-5 years of progressive experience in data engineering, data integration, or related technical roles.
 * Strong experience with SQL, Python, and modern data engineering frameworks and tools.
 * Experience developing and maintaining large-scale data pipelines and data integration solutions.
 * Knowledge of data warehousing, data modeling, ETL/ELT processes, and database design principles.
 * Experience with cloud data platforms and services in Azure and/or GCP.
 * Experience with DataOps practices, CI/CD pipelines, workflow orchestration, and automated testing.
 * Experience working with big data technologies, distributed processing frameworks, or cloud-native data architectures is preferred.
 * Strong analytical and problem-solving skills with the ability to translate business requirements into technical solutions.
 * Excellent communication and collaboration skills with both technical and non-technical stakeholders.

## Description

CI/CD pipelines
dataops tools
data governance
data modeling
data warehousing
troubleshooting skills
data engineering
Python
ETL/ELT

 * Design, develop, and maintain scalable data pipelines and ETL/ELT processes to support enterprise data initiatives.
 * Build and optimize data integrations from multiple internal and external sources, ensuring data quality, integrity, and reliability.
 * Develop and manage data models, data warehouses, and data lakes that support reporting, analytics, and operational workloads.
 * Implement data engineering best practices, including automation, monitoring, testing, and performance optimization.
 * Work with cloud-based data platforms and services in Azure and/or GCP environments.
 * Support DataOps practices, including CI/CD pipelines, orchestration, and infrastructure automation.
 * Partner with data analysts, data scientists, business stakeholders, and application teams to understand requirements and deliver scalable data solutions.
 * Troubleshoot and resolve data issues while ensuring compliance with data governance and security standards.
 * Document processes, architectures, and technical solutions to support maintainability and knowledge sharing.