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Remote, mid-level AWS data role with broad skillset attracts many qualified applicants.
Core AWS data engineering skills are transferable but require cloud and data domain experience.
Explicit 5+ years requirement and many mandatory AWS/data stack skills create stringent shortlisting filters.
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Design, build, and maintain data pipelines and applications on AWS, focusing on data lakes, data warehouses, and ETL/ELT processes.
Oversee architecture and implementation of end-to-end data solutions including ingestion, storage, integration, processing, and access on AWS.
Lead and manage a team of data engineers, collaborate cross-functionally, improve data processes, and optimize for performance and cost.
Bachelor’s degree in Computer Science, Software Engineering, MIS, or equivalent.
5+ years experience implementing and supporting data lakes, data warehouses, and data applications on AWS in large enterprises.
Proficiency in Python, Shell scripting, SQL; strong working knowledge of AWS services including Glue, Redshift, S3, Lambda, EMR/Spark, Kinesis, SQS, CloudFormation, Athena, RDS, DynamoDB, Step Functions, IAM, KMS, Secrets Manager.
Experience with serverless application development, data pipeline orchestration, ETL/ELT, data modeling, and cloud-based data warehouse architecture.
Experienced in leading complex data engineering projects at scale with cross-functional teams in enterprise environments.
Hands-on with AWS native data services and serverless architectures, comfortable with rapid prototyping and production-grade implementations.
Able to independently architect, develop, test, and optimize high-velocity streaming and batch data pipelines with focus on scalability, cost, and reliability.