Data Engineer III AI, Automation, & Data Engineering
Boston University Boston, Massachusetts, United States · $120K–$150K/yr
Higher Education · 51-200 employees
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About the role
Design and maintain scalable DAG-based data pipelines and infrastructure to support institutional research and analytics. Collaborate with cross-functional teams to optimize ETL workflows, ensure data quality, and implement robust data governance standards.
What they look for
Requirements
Requires at least 3 years of experience in data engineering, backend development, and proficiency in Python and SQL. A degree in Computer Science or a related field is required, with a Master's degree preferred.
Benefits
Full description
Boston University Information Services & Technology (IS&T) is seeking applicants with diverse skills and experiences to join our innovative and inclusive community. Join us as a Data Engineer III, helping advance the university's data and analytics capabilities by designing and scaling modern data pipelines and platforms. Your work will directly support institutional reporting, research, and strategic initiatives. You will work on modern data infrastructure using tools, collaborate with data architects, analysts, and application developers to design robust ETL workflows, optimize performance, and ensure data quality and governance at scale. The Data Engineering team within IS&T plays a critical role in enabling data-driven decision-making across Boston University. We develop and maintain data infrastructure that supports analytics, institutional research, academic planning, and operational efficiency. Our team partners closely with data consumers across the university to ensure the reliability, scalability, and quality of enterprise data assets. You Will Design and maintain scalable DAG-based data pipelines using Dagster. Build and optimize data models in PostgreSQL using DBT for analytical and operational use cases. Develop batch and streaming ETL processes leveraging AWS services (S3, Glue, Athena, SNS/SQS, Lambda). Deploy and manage containerized workloads on Kubernetes, using infrastructure-as-code. Implement robust data quality checks, observability, and automated validation. Define schemas, data contracts, and transformation logic with cross-functional teams. Contribute to CI/CD workflows (e.g., GitHub Actions, ArgoCD) to perform code reviews and document technical standards.
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