Data Engineer, AWS & AI/ML Enablement

📋 Description

• • As a Data Engineer specializing in AWS & AI/ML Enablement at College Board, you will be instrumental in designing, constructing, and maintaining robust, scalable, and secure data platforms. These platforms are the bedrock for advanced analytics, comprehensive reporting, and cutting-edge AI/ML applications, directly impacting higher education recruitment and student engagement.
• • You will operate within a dynamic, AWS-native, microservices environment, fostering close collaboration with Product Owners, Architects, Software Engineers, and Data Scientists. Your work will transform raw data into trusted, actionable insights and AI-driven capabilities that significantly benefit students and higher education partners.
• • A significant portion of your role (60%) will focus on Data Engineering & Platform Development. This involves architecting and implementing scalable batch and streaming data pipelines utilizing a suite of AWS services including S3, Glue, Lambda, Kinesis, Step Functions, Redshift, Athena, and DynamoDB. You will be responsible for developing and optimizing sophisticated data models and complex SQL queries to serve analytics, reporting needs, and downstream consumers.
• • You will build and manage serverless ETL frameworks designed for automated ingestion, transformation, and loading of diverse data types. A key aspect of this role is the implementation of cloud-first, microservices-based architectures, ensuring high availability, optimal performance, and cost-effectiveness. Data quality, reliability, and observability will be paramount, achieved through rigorous automated testing, validation, monitoring, and alerting mechanisms.
• • Integration with Business Intelligence and analytics tools, such as QuickSight, will be crucial to enable real-time and self-service analytics capabilities for stakeholders. Furthermore, you will contribute to robust CI/CD pipelines, infrastructure automation, and secure development practices, ensuring the delivery of production-grade data systems.
• • The remaining 25% of your time will be dedicated to AI/ML & Advanced Analytics Enablement. This involves partnering closely with Data Science and AI teams to productionize ML-ready datasets, including the development of training, evaluation, and inference data pipelines. You will build and maintain feature pipelines and embedding workflows essential for ML models and experimentation.
• • You will actively support MLOps/LLMOps workflows, encompassing dataset versioning, experiment tracking, and the capture of inference data to drive continuous model improvement. Your contributions will enable critical AI use cases such as recommendation systems, personalization engines, and retrieval-augmented generation (RAG) through the creation of solid data foundations.
• • A thoughtful approach to AI feasibility, fairness, and effectiveness will be applied, particularly when handling sensitive or regulated data. The final 15% of your role will focus on Collaboration, Quality & Mentorship. You will actively participate in Agile/Scrum ceremonies, design reviews, and peer code reviews, fostering a culture of continuous improvement and knowledge sharing.
• • Cross-functional collaboration with Product, UX, Infrastructure, and Security teams will be essential to ensure holistic and effective solutions. You will have the opportunity to mentor junior engineers, guiding them on data architecture, coding standards, and best practices. Producing clear documentation, runbooks, and technical guides will be vital for long-term platform sustainability and knowledge transfer.
• • This role demands a proactive approach to problem-solving, a commitment to delivering high-quality, user-focused data products, and a passion for leveraging data and AI to enhance educational opportunities for students.