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Cloud Data & Machine Learning Solutions Architect - Public Sector

Amazon Web Services, Inc.Arlington, Virginia🇺🇸United StatesPosted Sep 17, 2026

Quick Overview

Seniority
Mid Senior
Employment type
Full Time
Work mode
Hybrid
Location
Arlington, Virginia, United States
SQLScalaAWSETLMachine LearningTableauHadoopPythonRedshiftTerraform

Job Description

Amazon Web Services, Inc. seeks a Delivery Consultant, Data to help public sector customers design and implement secure, scalable data and machine learning solutions on AWS. You will assess existing environments, define target architectures, and build data lakes, warehouses, and analytics pipelines using services like Redshift, Glue, EMR, and SageMaker. Partner with customer and AWS teams to lead migrations, optimize performance and cost, and enable self-service analytics. You will create technical artifacts, deliver workshops, and mentor teams in a fast-paced, customer-obsessed, learning-focused culture.

Responsibilities

  • Design and deliver AWS-based data and machine learning architectures for public sector customers
  • Build and optimize data lakes, warehouses, and analytics/ML pipelines using AWS services
  • Lead data platform and analytics migrations to AWS from on-premises or other clouds
  • Collaborate with customer stakeholders to define requirements, success metrics, and roadmaps
  • Create technical designs, deployment artifacts, and best-practice documentation
  • Deliver workshops, demos, and enablement sessions on AWS data and ML services
  • Diagnose and resolve performance, reliability, and cost optimization issues
  • Ensure security, compliance, and governance best practices in data solutions
  • Partner with internal AWS teams to align solutions with AWS services and innovations
  • Mentor customer and AWS team members to build long-term data and ML capabilities

Required Skills

  • AWS data services (Redshift, EMR, Glue, RDS)
  • Machine learning on AWS (Sage
  • Maker)
  • SQL and data modeling
  • Python or Scala programming
  • ETL/ELT pipeline design
  • Data warehousing and lake architectures
  • Cloud solution architecture
  • Infrastructure as Code (Cloud
  • Formation/Terraform)
  • Big data frameworks (Spark, Hadoop)
  • Analytics and BI tools (e.g., Quick
  • Sight, Tableau)

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