Cloud Analytics Platform Engineer (Senior Associate)

Huron; United States; Remote; USD 120,000 - 165,000; Full time

Benefits offered by this job

Medical, dental, vision coverage

401(k) match

Stock purchase plan

Paid time off

Job summary

Huron’s Data Forge team seeks an Analytics Platform Engineer to design, build, and operate the enterprise cloud data platform in AWS. You will develop scalable data and analytics solutions, automate capabilities, and improve reliability for multiple product teams.

The role focuses on Python development, data pipelines, and cloud-native architectures, with emphasis on onboarding datasets, automation, and delivering self-service analytics across the organization.

Qualifications

• Bachelor's degree or higher in Computer Science, Information Systems, Engineering, Analytics, or related field.
• 2–5 years of software, data, or platform engineering experience.
• Strong Python and production-grade application development.
• Hands-on AWS experience with S3, Lambda, Glue, Athena, ECS, Step Functions, CloudWatch, IAM.
• Experience with data pipelines, SQL, and CI/CD pipelines.

Responsibilities

• Design, develop, and maintain platform services for data ingestion, transformation, analytics, and reporting.
• Build reusable Python framework components and automation tools used across teams.
• Develop data pipelines and ETL/ELT solutions using Python, Spark, and AWS services.
• Implement monitoring, observability, and platform automation.

Skills

Python development

AWS experience

SQL

CI/CD

Troubleshooting

Education

Bachelor's degree in CS/Engineering/Analytics

Tools

Terraform

CloudFormation

Apache Spark

Apache Iceberg

GitHub Enterprise

Azure DevOps

Job description

Huron’s Data Forge team seeks an Analytics Platform Engineer to design, build, and operate the enterprise cloud data platform in AWS. You will develop scalable data and analytics solutions, automate capabilities, and improve reliability for multiple product teams.

The role focuses on Python development, data pipelines, and cloud-native architectures, with emphasis on onboarding datasets, automation, and delivering self-service analytics across the organization.