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Senior AWS Data Engineer (Full-Time, Onsite 4 Days/Week)

carbonsoft β€’ United State
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AI Summary

Design, develop, and maintain scalable AWS-based data pipelines and cloud solutions as a Senior AWS Data Engineer. Leverage Python, SQL, and ETL/ELT frameworks to build high-performance data architectures while ensuring reliability, security, and collaboration with cross-functional teams. Requires 7+ years of hands-on experience in AWS data services and distributed data processing.

Key Highlights
7+ years of experience in Data Engineering with a focus on AWS cloud services
Design and optimize scalable ETL/ELT pipelines using AWS Glue, S3, Redshift, and PySpark
Collaborate with data teams to ensure data quality, security, and governance in production environments
Key Responsibilities
Design and develop scalable ETL/ELT data pipelines on AWS using AWS Glue, S3, Lambda, and Redshift
Build and optimize data pipelines using Python/PySpark for efficient large-scale data processing
Monitor, troubleshoot, and optimize data pipelines and AWS workloads while ensuring data quality, security, and reliability
Technical Skills Required
Amazon Web Services Python SQL
Benefits & Perks
All visa types accepted
Nice to Have
Experience with Snowflake or other modern data platforms
Familiarity with AWS Step Functions or Airflow for workflow orchestration
CI/CD and DevOps practices for data engineering workflows

Job Description


AWS Data Engineer – W2

πŸ“ Location: Richmond, VA

🏒 Work Arrangement: Onsite – 4 days/week from Day 1

πŸ’Ό Employment Type: W2 – Full-Time

πŸ›‚ Visa: All visa types accepted

Job Summary

We are looking for an experienced AWS Data Engineer to design, develop, and maintain scalable data pipelines and cloud-based data solutions. The ideal candidate will have strong hands-on experience with AWS data services, Python, SQL, and ETL development.

Key Responsibilities

  • Design and develop scalable ETL/ELT data pipelines on AWS.
  • Develop data solutions using AWS Glue, S3, Lambda, Redshift, and related services.
  • Build and optimize data pipelines using Python/PySpark.
  • Develop complex SQL queries and perform data transformation and optimization.
  • Work with large datasets and implement efficient data processing solutions.
  • Monitor, troubleshoot, and optimize data pipelines and AWS workloads.
  • Collaborate with data architects, analysts, developers, and business teams.
  • Ensure data quality, security, governance, and reliability.

Required Skills

  • 7+ years of experience in Data Engineering.
  • Strong hands-on experience with AWS.
  • Experience with AWS Glue, S3, Redshift, Lambda and cloud data services.
  • Strong Python/PySpark and SQL skills.
  • Experience building production-grade ETL/ELT pipelines.
  • Strong understanding of data warehousing and data modeling.
  • Experience with Spark and distributed data processing.
  • Strong troubleshooting and problem-solving skills.

Preferred

  • Experience with Snowflake or other modern data platforms.
  • Experience with AWS Step Functions / Airflow.
  • Familiarity with CI/CD and DevOps practices.
  • Experience working in Agile environments.

Interested candidates can share their resume for consideration.


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