AWS Data Engineer & Big Data Platform Lead

Veriipro

Englewood (CO)

On-site

USD 120,000 - 170,000

Full time

14 days+

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Job summary

Veriipro is seeking an experienced AWS Data Engineer / Big Data Technology Lead to design, build, and maintain scalable data solutions on AWS, delivering enterprise-grade analytics. You will lead data pipelines, data lakes and warehouses, and collaborate with data scientists and business teams to productionize datasets, ensure quality, and optimize performance.

This role requires 7+ years in Big Data or Data Engineering, strong SQL, Hadoop experience, and a DevOps mindset for automated

Qualifications

7+ years of experience in Big Data Engineering or related roles.
Strong hands-on experience with AWS cloud services and big data technologies.
Experience designing, developing, and maintaining scalable data pipelines and cloud-based architectures.
Proficiency in ETL/ELT pipelines using AWS services such as AWS Glue, Lambda, Kinesis, and Step Functions.
Experience building and managing data lakes and data warehouses using AWS services including S3, Athena, Redshift, and RDS.
Strong knowledge of data modeling, data integration, and ensuring data quality and integrity.
Experience with Hadoop and other Big Data technologies.
Strong SQL skills with experience in databases such as Oracle 10g/11g/12c and SQL Server.
Experience with Unix/Linux environments and scripting.
Familiarity with DevOps tools such as Jenkins and Maven.
Experience with monitoring and logging tools such as Splunk is preferred.
Ability to collaborate with data scientists, analysts, and cross-functional teams to deliver production-ready data solutions.

Responsibilities

Design, develop, and maintain scalable data pipelines and architectures on AWS.
Develop ETL/ELT solutions using AWS services.
Build and manage cloud-based data lakes and data warehouses using S3, Athena, Redshift, and RDS.
Design scalable data architectures while ensuring data quality, security, and integrity.
Develop data processing workflows to support large-scale ingestion and transformation.
Collaborate with data scientists, analysts, and business teams to curate production-ready datasets.
Monitor, troubleshoot, and improve data pipeline performance and reliability.
Work with DevOps practices and deployment automation (Jenkins, Maven).