AWS Cloud Data Engineer
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Summary
Designs and builds AWS-based data pipelines and feature stores using Python, SQL, and tools like Glue and Sagemaker to support analytics and ML workloads.
- Proficiency with databases (e.g., Snowflake, DB2, Redshift) and dimensional modeling.
- Hands-on experience with AWS architecture and services (Lambda, Glue, Kinesis, Firehose, Athena, S3, Cloudwatch, Dynamodb, API Gateway).
- Proficient in Python, SQL, and scripting (e.g., Unix shell scripts).
- Experience building feature engineering pipelines.
- Experience with CI/CD tools such as GitHub, GitHub Actions, CodePipeline, and CloudFormation.
- Knowledge of user authentication and authorization across systems, servers, and environments.
- Experience with Tecton or Sagemaker or similar feature stores.
- Experience with NoSQL databases.
- Ability to take ownership and proactively ensure delivery timelines are met.
Good-to-Have Skills:
- Experience in data pipeline development using modern ETL tools, specifically Informatica PowerCenter and/or Informatica IICS.