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Job Description & How to Apply Below
We are looking for a skilled AWS Data Engineer who can design, build, and maintain scalable data pipelines and data platforms on AWS. The ideal candidate should have strong experience with AWS big data services, ETL/ELT pipelines, SQL, Python, and modern data engineering frameworks.
Key Responsibilities
Data Pipeline Development
Design and build scalable, secure ETL/ELT pipelines on AWS.
Ingest, transform, and process structured/unstructured data from various sources (RDS, APIs, S3, files, streaming, etc.).
Implement data lake and data warehouse solutions on AWS.
AWS Services Expertise
Build workflows using AWS Glue (ETL Jobs, Crawlers, Catalog).
Develop and optimize data pipelines using AWS Lambda, Step Functions, SNS/SQS.
Manage data storage using Amazon S3 (buckets, lifecycle, partitions).
Work with AWS EMR / Spark for big data processing.
Use Amazon Redshift for data warehousing and analytics.
Configure data ingestion using Amazon Kinesis / MSK for real-time streaming.
Optimization & Data Quality
Optimize Spark/Glue jobs for performance and cost.
Build data validation, monitoring, and error-handling frameworks.
Implement best practices for data quality, governance, and metadata management.
Collaboration
Work with Data Architects, Analysts, and BI teams to deliver analytics-ready datasets.
Support reporting tools like Quick Sight, Tableau, or Power BI.
Participate in Agile development processes (sprints, daily stand-ups).
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