The ideal candidate will have expertise in designing and implementing data architectures using AWS services such as S3, Athena, Redshift, or DynamoDB. They should be well-versed in developing data ingestion pipelines and data integration processes to ensure seamless data transfer from various sources into the DataLake.
Additionally, they must possess strong skills in implementing data transformation and enrichment processes using serverless technologies like AWS Lambda, Glue, or similar tools. The ability to collaborate with data scientists and analysts to understand their data requirements and design appropriate data models and schemas is also crucial.
* Main Responsibilities
* Designing and Implementing Data Architectures: Utilize AWS services to create efficient data storage and retrieval mechanisms.
* Developing Data Pipelines: Create seamless data transfer processes from various sources into the DataLake.
* Data Transformation and Enrichment: Leverage serverless technologies to implement data quality and consistency measures.
* Collaboration: Work closely with data scientists and analysts to understand data requirements and design suitable data models and schemas.
The role requires continuous evaluation of new AWS services and technologies to enhance the DataLake architecture, improve data processing efficiency, and drive innovation. Proficiency in mentoring junior data engineers and collaborating with cross-functional teams to deliver high-quality solutions within defined timelines is also necessary.
Key Skills and Qualifications
* Expertise in designing and implementing data architectures using AWS services
* Strong skills in developing data ingestion pipelines and data integration processes
* Experience with implementing data transformation and enrichment processes using serverless technologies
* Ability to collaborate with data scientists and analysts
* Continuous learning and adaptation of new AWS services and technologies
Benefits of Working in This Role
* Foster a culture of unlimited learning and opportunities for improvement and mutual development
* Drive innovation and enhance the DataLake architecture
* Improve data processing efficiency and deliver high-quality solutions