Design, build, and maintain scalable ETL/ELT pipelines using GCP-native technologies
Develop and optimize large-scale data processing frameworks for enterprise data platforms
Work closely with cross-functional teams including Data Scientists, Analysts, Architects, and Business stakeholders
Implement cloud-native data engineering solutions leveraging GCP services
Ensure data quality, governance, scalability, and performance optimization across data platforms
Participate in architecture discussions and contribute to technical solution design
Leverage AI-assisted engineering tools such as GitHub Copilot, Claude, ChatGPT, or similar platforms to accelerate development and improve engineering efficiency
Mentor junior engineers and provide technical guidance across the project lifecycle
Collaborate within Agile teams to deliver high-quality data engineering solutions
Must Have Skills
6 years of minimum experience in Data Engineering
Strong hands-on expertise in Python and SQL
Extensive experience with Google Cloud Platform (GCP)
Expertise in building ETL/ELT pipelines and large-scale data processing solutions
Strong understanding of cloud-based data architectures and distributed data systems
Exposure to AI-assisted engineering tools such as GitHub Copilot, Claude, ChatGPT, or similar platforms
Strong problem-solving, debugging, and performance optimization skills
Good to Have Skills
Experience working with Clinical Trial, EMR/EHR, or Healthcare data platforms
Understanding of Healthcare data standards and workflows
Exposure to modern data warehousing and analytics platforms
Experience working in regulated or healthcare environments
Preferred Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or related field
Strong communication and stakeholder management skills
Experience leading technical initiatives and mentoring engineering teams