AWS Data Engineer with Redshift, Lambda, Glue expertise

At Tredence AWS Data Engineer designs, builds, and maintains scalable data pipelines and infrastructure using AWS services like Glue, S3, and Redshift, focusing on data ingestion, transformation (ETL), modeling, and quality. They write SQL and Python code, orchestrate workflows with tools like Airflow, and collaborate with data scientists and analysts, aiming to ensure data is accurate, accessible, and optimized for analysis and business decision-making. 

Role – AWS Data Engineer
Years of Experience: 2~5 years

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  The day-to-day work of a junior-to-mid AWS data engineer involves a mix of development, monitoring, and collaboration.
  


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  Pipeline development: Designing, building, and maintaining robust and scalable ETL/ELT (Extract, Transform, Load) pipelines on AWS.
  


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  Managing and optimizing cloud-based data warehouses like Amazon Redshift to support business intelligence and analytics.
  


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  Implementing validation and cleansing techniques to ensure data accuracy and reliability throughout the pipeline.
  


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  Managing data storage (e.g., S3), transformation services (e.g., Glue), and orchestration tools (e.g., Airflow/MWAA).
  


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  Working closely with data analysts, data scientists, and business stakeholders to understand data requirements and deliver reliable data solutions.
  


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  Monitoring and tuning data workflows and database queries to ensure fast and efficient processing.
  


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  Creating clear and comprehensive documentation of data processes, infrastructure, and technical specifications.