Data & Cloud Services Engineer

SCI SOLUTIONS GLOBAL•Plano, TX

About The Position

Our client is seeking a Data & Cloud Services Engineer to build and support AWS data lake solutions, Snowflake platforms, and Python-based data pipelines. The role also involves infrastructure automation, CI/CD, AWS deployments, and appropriately using AI-assisted engineering tools to improve development productivity. The position is located in Plano, Texas. The client prefers candidates authorized to work in the United States as a U.S. citizen, Green Card holder, or Green Card EAD holder. W2 employment is required; C2C, 1099, and third-party profiles are not accepted.

Requirements

  • At least 6 years of cloud data engineering experience.
  • Experience with AWS Glue, S3, Lambda, Step Functions, and Lake Formation.
  • Experience with Snowflake.
  • Experience with Python.
  • Experience with Terraform or other Infrastructure as Code.
  • Experience with CI/CD pipelines and AWS deployments.
  • Familiarity with AI-assisted tools such as GitHub Copilot, Claude, or agentic workflows.
  • Additional technical experience with AWS Data Lake services, including Lake Formation, Glue, Step Functions, and Lambda.
  • Experience with data movement using AWS CLI, Python APIs, Spark, or Boto3.
  • Experience with analytics tools including Athena, S3 Select, EMR, or Redshift.
  • Experience with S3 and EC2.
  • Experience with CI/CD and change management tools including Bitbucket/Git, Jenkins, or ServiceNow.
  • Experience with monitoring tools including AWS CloudWatch or Splunk.

Nice To Haves

  • Familiarity with Control-M.
  • Java or Scala experience.
  • Experience with Snowflake Horizon Catalog.
  • Experience with Databricks and Unity Catalog.
  • Additional experience with Spark and cloud data analytics.

Responsibilities

  • Build and support AWS data lake solutions.
  • Build and support Snowflake platforms.
  • Develop Python-based data pipelines.
  • Use infrastructure automation and Infrastructure as Code.
  • Work with CI/CD pipelines and AWS deployments.
  • Use AI-assisted engineering tools appropriately to improve development productivity.
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