Google Cloud Data Engineering

As an experienced Data Engineer with 8+ years of experience, your role will involve designing and implementing scalable, reliable, and efficient data pipelines and architectures across Google products and services. You will be responsible for developing and maintaining data models, schemas, and ontologies to support various data sources and use cases. Additionally, you will evaluate and recommend new data technologies and tools to enhance infrastructure and capabilities. Key Responsibilities: - Collaborate with product managers, engineers, and researchers to define data requirements and deliver robust technical solutions. - Build and optimize batch and real-time data pipelines using Google Cloud Platform (GCP) services like Dataflow, Dataproc, Pub/Sub, and Cloud Functions. - Implement data quality checks and validation processes to ensure consistency and accuracy. - Design and enforce data governance policies to maintain data security and regulatory compliance. - Design and manage scalable storage solutions with GCP services including BigQuery, Cloud Storage, and Spanner. - Optimize data retrieval and storage strategies for performance and cost-efficiency. - Implement data lifecycle management practices and archival strategies. Qualifications Required: - Bachelors or Masters degree in Computer Science, Engineering, or a related field. - Strong knowledge of data warehousing, data modeling, and ETL development. - Proven expertise in designing and implementing large-scale data architectures and pipelines. - Proficiency in SQL and at least one programming language such as Python or Java. - Hands-on experience with GCP services like BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage. - Familiarity with open-source data tools like Hadoop, Spark, and Kafka. - Excellent communication and collaboration skills. In addition to the above responsibilities and qualifications, it is preferred that you have experience with data governance, data quality frameworks, and compliance. Exposure to machine learning and data science workflows, as well as familiarity with containerization and orchestration technologies such as Docker and Kubernetes, would be beneficial. Any contributions to open-source projects or technical communities, along with holding a Google Cloud Professional Data Engineer certification, would be regarded as a plus.,