Bentley University is looking to modernize its data infrastructure to support advanced analytics and predictive modeling. The Cloud Data Engineer will be instrumental in building and maintaining scalable and secure data pipelines within Bentley's cloud data ecosystem, ensuring data accessibility and reliability for various stakeholders.
Requirements
- Minimum of two years of experience with the Azure data ecosystem, including tools such as Azure Synapse, Microsoft Fabric, and Azure Data Factory.
- Experience with Snaplogic for data integration and pipeline development.
- Proficiency in writing and optimizing SQL queries.
- Hands-on experience with Python or R for data wrangling, transformation, and basic ML tasks.
- Exposure to ML operations workflows and tools.
- Understanding of data modeling, ETL/ELT pipelines, and structured/semi-structured data processing.
- Experience supporting predictive analytics initiatives (e.g., churn models, student success models, forecasting).
Responsibilities
- Participate in Bentley’s data modernization efforts by developing cloud-based data pipelines and analytics infrastructure that support data engineering, analytics and predictive modeling needs.
- Build and maintain ETL/ELT processes to extract, transform, and load data from enterprise systems into cloud-based platforms such as Azure Synapse, Microsoft Fabric, and data lakes.
- Collaborate with data power users to design and implement infrastructure that supports analytics and machine learning workflows, including feature engineering pipelines and model outputs integration.
- Use Python or R to support data preparation, transformation, and integration tasks, and assist in developing reusable scripts and tools for analytics teams.
- Assist in the deployment and scaling of predictive models within Bentley’s cloud environment to support initiatives such as student success analytics.
- Design and support data warehouse structures (e.g., star schemas, fact and dimension tables) in alignment with reporting and ML readiness.
- Monitor and troubleshoot data jobs and model pipelines to ensure quality, integrity, and timely delivery of data.
Other
- Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field.
- Minimum of two consecutive years of post-graduate professional experience in data engineering or a related field is required, ideally with exposure to data science and machine learning infrastructure.
- Ability to collaborate with cross-functional teams including data analysts, data scientists, and IT professionals.
- Partner with business users to clarify data and reporting requirements and ensure data pipelines meet evolving analytical needs.
- Some campus travel may be required for meetings and collaboration.
