AWS Data Engineer

Greenwood Village, CO - USA

Monthly Salary: Not Disclosed

Posted on: 17 hours ago

Vacancies: 1 Vacancy

Job Summary

Hello Folks
Hope you are doing good!
Please find the below requirement and let me know your interest

Job Title: AWS Data Engineer
Location: Greenwood Village CO
Mode Of Work: 4 Days Onsite
W2 Role
Visa: GC GC EAD H4 EAD & USC only

Job Description
Overview: We are partnering with a leading enterprise client to hire a Senior Data Engineer for a contract opportunity supporting a large-scale mobile data platform. This role sits within a data architecture team responsible for designing and evolving high-volume data systems that power critical business insights. The team is currently supporting a high-priority strategic initiative creating the need for an experienced engineer to own and stabilize core platform responsibilities. This is a hands-on opportunity with meaningful ownership and architectural exposure. You will take ownership of day-to-day data engineering responsibilities within a large-scale batch processing ecosystem.

Key Responsibilities Include:

Monitor and ensure reliability and accuracy of ETL processes
Improve and automate data pipeline monitoring
Maintain and enhance Spark and SQL-based transformations
Design and implement new data integrations
Optimize AWS jobs and storage for performance and cost efficiency
Contribute to data architecture standards and engineering best practices
This role plays a critical part in maintaining the stability and scalability of the platform
Technical Environment: 300 Spark-based batch jobs
Heavy SQL-based transformations
AWS ecosystem (S3 Athena Spark)
Large-scale batch processing (non-real time)
150 critical daily data transformations feeding downstream systems
Daily reliability is essential as downstream tools and stakeholders depend on consistent data delivery

Please share with me his visa copy DL copy and below mentioned details.

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Thanks
Jagdish Manager IT Staffing
T
6095 Marshalee Dr Suite 250 Elkridge MD 21075

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Required Experience:

Monitor and ensure reliability and accuracy of ETL processes
Improve and automate data pipeline monitoring
Maintain and enhance Spark and SQL-based transformations
Design and implement new data integrations
Optimize AWS jobs and storage for performance and cost efficiency
Contribute to data architecture standards and engineering best practices
This role plays a critical part in maintaining the stability and scalability of the platform
Technical Environment: 300 Spark-based batch jobs
Heavy SQL-based transformations
AWS ecosystem (S3 Athena Spark)
Large-scale batch processing (non-real time)
150 critical daily data transformations feeding downstream systems
Daily reliability is essential as downstream tools and stakeholders depend on consistent data delivery