Cloud Data Engineer

Bentley University•Waltham, MA

59d•Hybrid

About The Position

The Cloud Data Engineer supports the design, implementation, and maintenance of data pipelines, and predictive analytics workflows within Bentley's cloud data ecosystem. Employer does not provide visa sponsorship for this position. This role plays a key part in sourcing, transforming, and delivering data from enterprise systems to data lakes and warehouses while supporting analytics and future data science initiatives through scalable and secure infrastructure. The position involves collaboration with a variety of stakeholders including data analysts, and IT teams to ensure data is accessible, reliable, and actionable.

Requirements

Bachelor's degree in Computer Science, Data Science, Information Systems, or a related field.
Minimum of three years of professional experience, including at least two consecutive years in data engineering or a related technical role.
Hands-on experience with the Azure data ecosystem, such as Azure Synapse, Microsoft Fabric, and Azure Data Factory; experience with other cloud platforms (e.g., AWS or GCP) is a plus.
Experience with data integration and pipeline development tools, such as SnapLogic, Informatica, or Azure Data Factory to design, automate, and maintain data flows across multiple systems.
Experience in data warehouse design and modeling, including star/snowflake schemas and Slowly Changing Dimension (SCD) techniques, using platforms such as Azure SQL or Oracle.
Proficiency in SQL for query development and optimization.
Hands-on experience with Python or R for data wrangling, transformation, and basic machine learning tasks.
Strong understanding of ETL/ELT processes structured and semi-structured data, and exposure to machine learning operations (MLOps) workflows and tools.
Demonstrated ability to collaborate effectively with cross-functional teams, including data analysts, data scientists, and IT professionals.

Nice To Haves

Experience supporting predictive analytics initiatives (e.g., churn models, student success models, forecasting).
Familiarity with visualization tools such as Power BI or similar.
Exposure to metadata management and data governance practices.
Experience in higher education or a similar mission-driven environment.
Understanding of Workday data structures, particularly Workday Learning or Student, is a plus.

Responsibilities

Participate in Bentley University's data modernization initiatives 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 platforms such as Azure Synapse and Microsoft Fabric.
Use programming languages such as SQL, PL/SQL, Python, and R, along with tools like Azure Data Factory, Alteryx, Power Query, SnapLogic, or Informatica, to support data preparation, transformation, and integration activities.
Develop reusable scripts and tools to streamline analytics workflows and improve data accessibility across teams.
Collaborate with data users and analysts to design infrastructure that supports analytics and machine learning workflows, including feature engineering pipelines and model output integration.
Assist in deploying and scaling predictive models within Bentley's cloud environment to support initiatives such as student success analytics.
Design and maintain data warehouse structures (e.g., star and snowflake schemas with fact, bridge, and dimension tables) using Slowly Changing Dimension (SCD) techniques in databases such as Azure SQL (serverless and dedicated) and Oracle.
Partner with business stakeholders to clarify data and reporting requirements and ensure that pipelines meet evolving analytical needs.
Monitor and troubleshoot data jobs and model pipelines to ensure data quality, integrity, and timely delivery.
Implement logging, alerting, and monitoring processes to ensure data reliability and performance.
Contribute to the development and maintenance of metadata management tools, data catalogs, and data dictionaries in collaboration with the Data Governance team.
Support secure data access practices and collaborate with cloud engineering, integration, and security teams to ensure compliance and operational reliability.