Google Data Engineer Jobs
Google Workspace Support Specialist
By ManTech
At United States
Strategy Manager, Google Workspace
By Google
At New York, NY, United States
Teacher, Google Children's Center
By Google
At , Mountain View
Customer Engineer, Data Analytics, Financial Services, Google Cloud
By Google
At Atlanta, GA, United States
Product Planner, Google Nest
By Google
At Mountain View, CA, United States
Software Engineer Iii, Google Nest
By Google
At Mountain View, CA, United States
Software Engineer Iii, Google Workspace
By Google
At New York, NY, United States
Google Analytics Jobs
By Charming Apparel
At United States
Software Engineer Iii, Google Cloud Data Management
By Google
At United States
Senior Software Engineer, Google Research
By Google
At Mountain View, CA, United States
Interaction Designer, Google Workspace
By Google
At United States
Google Ad Account Manager
By Monarchy Media
At United States
Software Engineer Iii, Google Workspace
By Google
At Sunnyvale, CA, United States
Google Analytics Data Specialist - Chicago
By Making Science
At Chicago, IL, United States
Interaction Designer, Google Workspace
By Google
At New York, NY, United States
Product Manager Ii, Google Workspace
By Google
At United States
Facilities Technician, Google Data Center
By Google
At , Midlothian, Tx
Engineering Manager, Google Pixel
By Google
At , San Diego, Ca
Are you an experienced Data Engineer looking to join a world-renowned tech giant? Google is looking for a Data Engineer to join their team and help them unlock the power of data. You'll be responsible for designing and developing data pipelines, creating data models, and optimizing data storage and retrieval. If you have a passion for data and a knack for problem-solving, this is the perfect opportunity for you!
Overview Google Data Engineers are responsible for designing, building, and maintaining data systems and pipelines to support data-driven products and services. They work with data scientists, product managers, and other stakeholders to ensure that data is collected, stored, and processed efficiently and accurately. Detailed Job Description Google Data Engineers are responsible for designing, building, and maintaining data systems and pipelines to support data-driven products and services. They work with data scientists, product managers, and other stakeholders to ensure that data is collected, stored, and processed efficiently and accurately. They are also responsible for developing and maintaining data models, ETL processes, and data warehouses. Google Data Engineers must have a strong understanding of data engineering principles and technologies, including SQL, NoSQL, Hadoop, and cloud computing. They must be able to design and implement data pipelines and ETL processes, and they must be able to troubleshoot and optimize data systems. They must also be able to work with stakeholders to ensure that data is collected, stored, and processed in a way that meets the needs of the organization. Skills Required• Strong knowledge of data engineering principles and technologies, including SQL, NoSQL, Hadoop, and cloud computing
• Ability to design and implement data pipelines and ETL processes
• Ability to troubleshoot and optimize data systems
• Ability to work with stakeholders to ensure that data is collected, stored, and processed in a way that meets the needs of the organization
• Knowledge of data visualization tools and techniques
• Knowledge of data security and privacy best practices
• Knowledge of data warehousing and data modeling
• Knowledge of scripting languages such as Python and R
• Knowledge of machine learning and artificial intelligence
Qualifications
• Bachelor’s degree in Computer Science, Information Systems, or a related field
• 5+ years of experience in data engineering
• Experience with data visualization tools and techniques
• Experience with data security and privacy best practices
• Experience with scripting languages such as Python and R
• Experience with machine learning and artificial intelligence
Knowledge
• Knowledge of data engineering principles and technologies, including SQL, NoSQL, Hadoop, and cloud computing
• Knowledge of data visualization tools and techniques
• Knowledge of data security and privacy best practices
• Knowledge of data warehousing and data modeling
• Knowledge of scripting languages such as Python and R
• Knowledge of machine learning and artificial
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