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Google Cloud Platform Data Lead
Job Summary We are seeking a skilled Data Analytics Engineer with strong experience in Google Cloud Platform (Google Cloud Platform) and expertise across modern data engineering and analytics tools. The ideal candidate will design, build, and optimize scalable data pipelines, ensure data quality, and enable advanced analytics and reporting through Big Query, Dataflow, Data form, Looker, and Python. This role bridges data engineering and analytics, ensuring reliable, accurate, and actionable insights for business stakeholders.
Key Responsibilities
- Design, develop, and maintain scalable ETL/ELT pipelines using Dataflow, Data form, and Big Query.
- Implement data modeling, transformation, and orchestration best practices for analytics workflows.
- Collaborate with data scientists, analysts, and business teams to deliver insights via Looker dashboards and reports.
- Ensure data reliability, quality, and governance across multiple data sources.
- Optimize Big Query queries, partitions, and clustering for performance and cost efficiency.
- Write Python scripts for automation, data processing, and integration with APIs.
- Implement CI/CD practices for data pipeline deployments.
- Monitor, troubleshoot, and improve data infrastructure performance and reliability.
- Document data models, pipelines, and dashboards to ensure transparency and maintainability.
Skill Requirements
- Design, develop, and maintain scalable ETL/ELT pipelines using Dataflow, Data form, and Big Query.
- Implement data modeling, transformation, and orchestration best practices for analytics workflows.
- Collaborate with data scientists, analysts, and business teams to deliver insights via Looker dashboards and reports.
- Ensure data reliability, quality, and governance across multiple data sources.
- Optimize Big Query queries, partitions, and clustering for performance and cost efficiency.
- Write Python scripts for automation, data processing, and integration with APIs.
- Implement CI/CD practices for data pipeline deployments.
- Monitor, troubleshoot, and improve data infrastructure performance and reliability.
- Document data models, pipelines, and dashboards to ensure transparency and maintainability.