Data Engineer III: AI-Driven Pipelines & Automation

SilkRoad; Boston (MA); On-site; USD 120,000 - 150,000; Full time

Benefits offered by this job

Time Off package

Retirement plan

Tuition Assistance

Job summary

Boston University IS&T is seeking a Data Engineer III to design and scale modern data pipelines, partnering with data architects, analysts, and application developers to deliver robust ETL workflows. You will optimize data quality and governance at scale, and contribute to CI/CD workflows and code reviews.

The role focuses on DAG-based pipelines with Dagster, DBT data models, and AWS-based batch/streaming ETL, deployed on Kubernetes.

Qualifications

• Bachelor's degree in CS/IS/Analytics or related field; 3+ years in data engineering.
• Master's degree preferred.
• Applicable experience accepted in lieu of degree.

Responsibilities

• 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.
• Define schemas, data contracts, and transformation logic with cross-functional teams.

Skills

Python

SQL

Dagster

Airflow

Kubernetes

Docker

Git

CI/CD

Data governance

Agile

Education

Bachelor's degree in Computer Science, Information Systems, Data Analytics, or Business Analytics

Master's degree preferred

Applicable experience accepted in lieu of degree

Tools

Dagster

Airflow

DBT

PostgreSQL

SQL Server

Kubernetes

Docker

GitHub Actions

ArgoCD

AWS (S3, Glue, Athena)

Job description

Boston University IS&T is seeking a Data Engineer III to design and scale modern data pipelines, partnering with data architects, analysts, and application developers to deliver robust ETL workflows. You will optimize data quality and governance at scale, and contribute to CI/CD workflows and code reviews.

The role focuses on DAG-based pipelines with Dagster, DBT data models, and AWS-based batch/streaming ETL, deployed on Kubernetes.