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Data Engineer, AWS & AI/ML Enablement
Company: College Board
Employment Type: Full-Time
Location: 100% Remote – USA (EST hours required)
Compensation: $140,000 – $151,000 (USD, location-adjusted)
Category: Data, AI & Analytics
Close date or Apply by date: Open until filled
WHY THIS ROLE MADE THE CUT
Fully remote (no hybrid or office requirements)
Full-Time role
Job is listed on company site with a direct link to apply
Legitimate, established company
Posted within the last 14 days OR includes a clear deadline to apply
Clear salary range is disclosed
ROLE SNAPSHOT
College Board is hiring a Data Engineer, AWS & AI/ML Enablement to design, build, and operate scalable, secure data platforms powering analytics, reporting, and emerging AI/ML use cases for their BigFuture Division serving higher education recruitment and student engagement. You'll design and maintain batch and streaming data pipelines using AWS services (S3, Glue, Lambda, Kinesis, Step Functions, Redshift, Athena, DynamoDB), develop and optimize data models and complex SQL queries, build serverless ETL frameworks for automated data transformation, partner with Data Science and AI teams to productionize ML-ready datasets and feature pipelines, support MLOps/LLMOps workflows including dataset versioning and experiment tracking, and implement cloud-first microservices architectures ensuring high availability and cost efficiency. This role requires 4+ years in Data Engineering with AWS production experience, strong Python and SQL proficiency, 1+ years building production-grade ML and GenAI solutions using AWS SageMaker and Amazon Bedrock, and solid understanding of DevOps, CI/CD, and microservices architectures.
KEY WORDS TO INCLUDE IN YOUR RESUME/COVER LETTER IF YOU APPLY:
AWS data engineering (S3, Glue, Lambda, Redshift, Athena)
Python and SQL proficiency
ETL/ELT pipeline development
AWS SageMaker and Amazon Bedrock
MLOps and feature pipelines
Microservices architectures
CI/CD and DevOps
Real-time data processing (Kinesis)
Data modeling and optimization
Cross-functional collaboration
Keywords are suggested based on the language used in the employer's job description to help applicants align with automated screening systems.
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