AWS Cloud Data Engineer
About the role
- Experience setting up AWS Data Platform – AWS Cloud
- Formation, Development
- EndPoints, AWS Glue, EMR and Jupyter/Sagemaker Notebooks, Redshift, S3, and EC2 instances.
- Track record of successfully building scalable Data Lake solutions that connects to distributed data storage using multiple data connectors.
- Must have a background in data engineering – Data Warehouse Development experience would be perfect
- Must have strong skills in SQL, Python, PySpark, and AWSExperience in designing, developing, optimizing and troubleshooting complex Data Pipelines using Spark cluster
- Ability to lead proofs-of-concepts and then effectively transition and scale those concepts into production at scale through, engineering, deployment and commercialization.
- Serve as an expert; envision and integrate emerging data technologies, anticipate new trends to solve complex business and technical problems.
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
