Senior Data Engineer - Azure, ETL & Analytics Platform

Harrisonst; Chicago (IL); On-site; USD 110,000 - 170,000; Full time

Job summary

Harrison Street Asset Management is seeking a Data Engineer to design, build, and maintain scalable data pipelines for analytics and decision support. You will transform and organize data across multiple domains, enabling analysts and data scientists to derive insights securely.

Responsibilities include creating data models, integrating diverse data sources, and ensuring data quality while collaborating with engineering, security, and business teams.

Qualifications

• Experience designing and maintaining data pipelines, ETL, and data platforms.
• Proficiency with Azure cloud services and Fabric/Data Factory.
• Strong SQL knowledge and experience with relational databases and data warehousing.
• Experience with Python/Java/Scala and data APIs.
• Ability to translate business needs into technical data solutions and communicate with stakeholders.

Responsibilities

• Design, build, and maintain scalable data pipelines and ETL processes into Enterprise Data Platform.
• Create data marts with dimensional modeling and optimized schemas.
• Integrate data from databases, SFTP, APIs, and streaming platforms with quality controls.
• Develop data models and data transformations for analytics and reporting.
• Collaborate with cross-functional teams and document data engineering practices.
• Monitor pipelines, troubleshoot, and upgrade systems for reliability.

Skills

Data pipeline

Azure

SQL

Python

Tableau

Power BI

Data modeling

Security compliance

Cross-functional collaboration

Tools

Azure Data Factory

Microsoft Fabric

Tableau

Power BI

Databricks

SQL databases

APIs

Job description

Harrison Street Asset Management is seeking a Data Engineer to design, build, and maintain scalable data pipelines for analytics and decision support. You will transform and organize data across multiple domains, enabling analysts and data scientists to derive insights securely.

Responsibilities include creating data models, integrating diverse data sources, and ensuring data quality while collaborating with engineering, security, and business teams.