Job Description
Project Role :
Project Role Description :
- Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems.
Must have skills : Google BigQuery
Good to have skills : NA
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education
Job summary
The Data Engineer role described in your summary falls under the Data Engineering function, which is a key part of the Data Management and Cloud Computing domains. Here's a breakdown of the function and how it aligns with the responsibilities of the role:
Function:
- Data Engineering:
- The primary function of this role is to design, develop, and maintain data solutions that ensure the proper collection, processing, and storage of data. This includes creating data pipelines, implementing ETL processes, and ensuring data quality, all of which are core responsibilities of a Data Engineer.
- Cloud Data Solutions:
- The specific mention of Google BigQuery as a required skill points towards cloud-based data solutions, particularly focusing on data warehousing and data processing in the cloud. As a Google BigQuery Data Engineer, the role is aligned with modern cloud architectures, ensuring that data solutions are scalable, reliable, and efficient.
- Data Management:
- The role also involves significant aspects of data quality assurance and data architecture, ensuring that data is accurate, consistent, and available for downstream applications. This highlights the importance of data governance and compliance in the Data Engineering function.
Key Responsibilities:
- Data Pipeline and Architecture Design: The role emphasizes designing and implementing data pipelines and managing data architecture, which involves working with large datasets, designing systems for efficient data processing, and optimizing data flow.
- ETL and Data Quality: Responsibilities include ETL processes (Extract, Transform, Load) and ensuring data quality, making sure that data is clean, well-structured, and accessible for analytical or business purposes.
- Collaboration and Decision Making: The Data Engineer is expected to work closely with multiple teams, leading decisions related to data architecture and design, while solving complex problems across different business functions.
- Technical Expertise: Proficiency in Google BigQuery for data processing, SQL, and scripting languages is essential for implementing efficient data systems. Additionally, expertise in cloud-based data solutions and data warehousing concepts is crucial.
Professional & Technical Skills:
- Proficiency in Google BigQuery: The candidate must have at least 7.5 years of experience with Google BigQuery, which is used for large-scale data analysis and data storage in cloud environments. This involves designing databases and ensuring the system can handle large data workloads effectively.
- Data Modeling and SQL: A strong background in data modeling and SQL is essential for building efficient database structures and writing queries to manipulate and retrieve data.
- Cloud Data Solutions: Experience with cloud platforms like Google Cloud, including tools like BigQuery for data storage and processing, is a key skill required in this role.
