(Remote) Google Data Engineer
2 days ago
Key Responsibilities
- Collaborate with stakeholders to understand data requirements and design effective data engineering processes and workflows using Google Cloud tools.
- Manage data extraction, transformation, and loading (ETL) processes, ensuring the timely and accurate ingestion of data into Google BigQuery.
- Design and implement data pipelines using Google Cloud services, such as Dataflow, Apache Beam, or Cloud Functions, to automate and optimize data processing.
- Develop and maintain data models and schemas in Google BigQuery to support efficient data storage, querying, and analysis.
- Implement data quality checks and validation processes to ensure data accuracy and consistency within the data platform.
- Work closely with data analysts and data scientists to understand their data requirements and provide the necessary infrastructure and tools for advanced analytics and machine learning.
- Collaborate with cross-functional teams, including DevOps and IT, to ensure data engineering processes align with overall system architecture and adhere to best practices.
- Implement a lakehouse architecture using Google Cloud tools, integrating structured and semi-structured data for analytics, reporting, and data exploration purposes.
- Monitor and optimize the performance of data processing workflows and data storage in Google BigQuery, identifying and resolving any bottlenecks or performance issues.
- Stay up-to-date with emerging trends, new features, and best practices in Google Cloud technologies and data engineering to continuously improve the data platform.
**Requirements**:
- Bachelor's or higher degree in Computer Science, Engineering, or a related field.
- Proven experience as a Data Engineer or similar role, with a focus on Google Cloud technologies and tools.
- Strong knowledge of the Google Cloud ecosystem, including Google BigQuery, Dataflow, Apache Beam, Cloud Functions, and related components.
- Experience in designing and implementing data processing workflows and ETL processes using Google Cloud tools.
- Proficiency in SQL and experience in working with structured and semi-structured data in Google BigQuery or similar data warehousing platforms.
- Familiarity with data lake and data warehouse concepts, and experience in implementing a lakehouse architecture using Google Cloud tools.
- Strong problem-solving skills and the ability to work independently and collaboratively in a fast-paced environment.
- Excellent communication and interpersonal skills to effectively collaborate with cross-functional teams and stakeholders.
- Google Cloud certifications, such as Google Certified Professional Data Engineer, are highly desired.
- Experience in the retail industry or understanding of retail data and analytics is a plus.
- Ability to thrive under pressure and manage time effectively.
- Good spoken and written English.
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