GCP Data Platform Engineer

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a GCP Data Platform Engineer based in the United States. As a GCP Data Platform Engineer, you will help build, maintain, and optimize cloud-based data platforms that support analytics, reporting, and data-driven products. You will work extensively with Google Cloud Platform (GCP), BigQuery, PostgreSQL, Python, and SQL to develop reliable data solutions and scalable infrastructure. Your work will involve designing data models, maintaining analytical datasets, and improving the pipelines that power critical business operations. You will also contribute to production support, database performance optimization, and the ongoing reliability of cloud data environments. This opportunity combines hands-on engineering with practical platform management in a remote, project-oriented setting. It is ideal for a technically skilled professional who enjoys solving data challenges, improving system performance, and delivering robust cloud solutions.

Cloud Data Platform Development: Develop, maintain, and enhance data platforms on Google Cloud Platform, ensuring scalability, reliability, and alignment with business and technical requirements. BigQuery Development & Optimization: Create and manage BigQuery datasets, tables, views, data models, and reporting pipelines to support analytics and downstream applications. PostgreSQL Database Management: Design and maintain PostgreSQL schemas, develop and optimize SQL queries, manage indexes, and support database migrations to improve performance and data accessibility. Python & SQL Engineering: Use Python and SQL to develop data processing logic, automate workflows, implement transformations, and maintain efficient data platform components. Data Pipeline Engineering: Build, maintain, and improve data pipelines that integrate information from multiple sources and deliver reliable, high-quality datasets for analytical and operational use. Data Modeling & Analytical Solutions: Design and maintain structured data models and analytical datasets that support reporting, business intelligence, and data-driven product development. Cloud Database Operations: Support database and platform operations, troubleshoot production issues, monitor system performance, and help maintain service availability and reliability. Performance Tuning & Troubleshooting: Identify bottlenecks in queries, databases, and data pipelines, implementing improvements to optimize processing efficiency and resource utilization. Platform Maintenance & Reliability: Perform ongoing maintenance, investigate data inconsistencies, resolve technical issues, and implement improvements that strengthen platform stability. Technical Collaboration: Work with engineering and other technical stakeholders to deliver dependable data solutions, resolve platform challenges, and support evolving project requirements. Requirements Google Cloud Platform Experience: Practical experience developing, supporting, or maintaining data platforms and cloud-based data solutions on GCP. BigQuery Expertise: Hands-on experience working with BigQuery datasets, tables, views, data models, or reporting and analytics pipelines. PostgreSQL Skills: Experience with PostgreSQL database development and maintenance, including schema design, SQL queries, indexing strategies, and database migrations. Python Proficiency: Strong working knowledge of Python for data engineering, automation, data processing, and platform development. SQL Expertise: Proficiency in SQL, including query development, data transformations, troubleshooting, and performance optimization. Data Pipeline Development: Experience building, maintaining, or improving data pipelines and analytical data solutions that integrate and process information from multiple sources. Data Modeling Knowledge: Understanding of data modeling principles, analytical dataset design, and the organization of data for reporting and downstream consumption. Production Support Experience: Ability to support production database environments and data platforms, diagnose technical issues, and maintain reliable operations. Problem-Solving & Analytical Thinking: Strong troubleshooting skills, attention to detail, and the ability to investigate performance issues and implement effective technical solutions. Collaboration & Communication: Ability to work effectively with technical teams, communicate complex issues clearly, and contribute to shared engineering objectives. Remote Work Readiness: Ability to work independently in a remote environment and manage technical responsibilities in a contract-based role. Employment Conditions: This is a contract opportunity based remotely in the United States. Applicants should be prepared to provide their current location, work authorization details, desired hourly rate, and current contract end date during the application process. Benefits Competitive Hourly Rate: Indicative compensation of $65–$75 per hour , depending on experience, technical expertise, and project requirements. Remote Work Flexibility: Work remotely from within the United States. Contract-Based Opportunity: Gain hands-on experience delivering cloud data engineering solutions in a project-driven environment. Modern Cloud Technologies: Work with Google Cloud Platform, BigQuery, PostgreSQL, Python, SQL, and other data engineering technologies. Technical Development: Strengthen your expertise in cloud data platforms, database management, data pipeline engineering, and performance optimization. Practical Engineering Experience: Contribute to real-world data platform development, production support, and analytical solution delivery. Collaborative Projects: Work with technical stakeholders to solve complex challenges and improve the reliability and efficiency of data infrastructure. Flexible Work Environment: Benefit from a remote work arrangement that supports independent, focused technical work. Employment Type: Contract. Experience Level: Entry-level listing, with practical experience in cloud data platforms, Python, SQL, and database technologies required.