Big Data Architect (Google Cloud Platform)

As an experienced Lead GCP Big Data Engineer, your role will involve designing and building scalable data pipelines, ETL/ELT workflows, and big data processing solutions on Google Cloud Platform (GCP). You will be expected to have a blend of hands-on development expertise and technical leadership skills to design up-to-date data architectures, optimize data workflows, and mentor engineering teams. Key Responsibilities: - Design, develop, and maintain scalable ETL/ELT data pipelines using PySpark, SQL, and GCP services. - Lead data engineering initiatives, provide technical leadership, and mentorship to the team. - Build and optimize data processing workflows using Dataflow, DataProc, Cloud Composer, and Apache Airflow. - Ensure data quality, governance, reliability, and performance optimization across data platforms. - Collaborate with cross-functional teams including data analysts, data scientists, and product teams to deliver end-to-end data solutions. - Implement best practices for data engineering, automation, monitoring, and deployment. - Troubleshoot and resolve data pipeline performance and scalability issues. Required Skills: - Google Cloud Platform (GCP): Storage, BigQuery, DataProc, Cloud Composer, DMS, Datastream, Analytics Hub, Workflows, Dataform, Datafusion, Pub/Sub, Dataflow. - Big Data Technologies: PySpark, Hadoop Ecosystem, ETL/ELT Frameworks, ANSI-SQL, Apache Airflow. - Programming Languages: Python, Java, Scala. Experience & Qualifications: - 810 years of experience in Data Engineering or Big Data Development. - Strong hands-on experience with GCP data services and big data processing frameworks. - Proven experience in designing and implementing large-scale distributed data pipelines. - Experience in data modeling, performance tuning, and workflow orchestration. - Strong problem-solving skills and the ability to work in a collaborative, fast-paced environment. Preferred Skills: - Experience in data governance, monitoring, and data quality frameworks. - Prior experience in leading teams or mentoring junior engineers. - Familiarity with CI/CD pipelines and DevOps practices for data engineering. As an experienced Lead GCP Big Data Engineer, your role will involve designing and building scalable data pipelines, ETL/ELT workflows, and big data processing solutions on Google Cloud Platform (GCP). You will be expected to have a blend of hands-on development expertise and technical leadership skills to design up-to-date data architectures, optimize data workflows, and mentor engineering teams. Key Responsibilities: - Design, develop, and maintain scalable ETL/ELT data pipelines using PySpark, SQL, and GCP services. - Lead data engineering initiatives, provide technical leadership, and mentorship to the team. - Build and optimize data processing workflows using Dataflow, DataProc, Cloud Composer, and Apache Airflow. - Ensure data quality, governance, reliability, and performance optimization across data platforms. - Collaborate with cross-functional teams including data analysts, data scientists, and product teams to deliver end-to-end data solutions. - Implement best practices for data engineering, automation, monitoring, and deployment. - Troubleshoot and resolve data pipeline performance and scalability issues. Required Skills: - Google Cloud Platform (GCP): Storage, BigQuery, DataProc, Cloud Composer, DMS, Datastream, Analytics Hub, Workflows, Dataform, Datafusion, Pub/Sub, Dataflow. - Big Data Technologies: PySpark, Hadoop Ecosystem, ETL/ELT Frameworks, ANSI-SQL, Apache Airflow. - Programming Languages: Python, Java, Scala. Experience & Qualifications: - 810 years of experience in Data Engineering or Big Data Development. - Strong hands-on experience with GCP data services and big data processing frameworks. - Proven experience in designing and implementing large-scale distributed data pipelines. - Experience in data modeling, performance tuning, and workflow orchestration. - Strong problem-solving skills and the ability to work in a collaborative, fast-paced environment. Preferred Skills: - Experience in data governance, monitoring, and data quality frameworks. - Prior experience in leading teams or mentoring junior engineers. - Familiarity with CI/CD pipelines and DevOps practices for data engineering.