Data Engineer

National Association of REALTORS•Chicago, IL

5h•$130,000 - $160,000•Hybrid

About The Position

NAR is seeking a Data Engineer to help build and operate the data infrastructure that supports analytics, reporting, and machine learning across the organization. This role focuses on turning complex, often fragmented data into reliable, production-ready pipelines and platforms that data scientists, analysts, and business teams can trust and use at scale. The emphasis is on operational data engineering rather than experimentation, with strong expectations around governance, data quality, security, and performance. The Data Engineer will design, implement, and maintain cloud-native data pipelines and storage architectures primarily within the Microsoft Azure ecosystem, with increasing alignment to Microsoft Fabric. The role works closely with research, IT, security, and product teams to ensure data is accessible, well-modeled, and production-ready while meeting compliance and governance standards common in regulated environments. This position plays a critical role in bridging data science prototypes to operational systems, ensuring models, dashboards, and analytics products are supported by robust, scalable data foundations. The ideal candidate is comfortable working across engineering, analytics, and governance concerns, with a practical focus on building systems that are stable, explainable, and actually used by the business.

Requirements

Bachelor's degree in computer science, Engineering, or related field (or equivalent experience).
6–8+ years of experience in data engineering with a strong focus on Microsoft Azure services.
Proven expertise in designing and implementing ETL/ELT pipelines using Azure Data Factory (ADF).
Hands‑on experience with Azure Data Lake Storage (ADLS Gen2), Blob storage, and scalable cloud storage patterns.
Strong skills in data modeling, including dimensional modeling and Lakehouse architectures.
Advanced proficiency in SQL (Azure SQL / MS‑SQL), including query optimization and performance tuning.
Experience automating data workflows using ADF, Azure Functions, Logic Apps, or other Azure-native automation tools.
Ability to ensure data quality, integrity, lineage, and security throughout the data lifecycle.
Strong problem‑solving skills for diagnosing pipeline failures, performance bottlenecks, and system issues.
Effective collaboration with cross‑functional teams including data scientists, analysts, and engineering teams.

Nice To Haves

Experience with Microsoft Fabric, Power BI, and analytics‑ready data design (optional but preferred).

Responsibilities

Lead architectural efforts for implementing the Data Science team's models and solutions
Design, implement, and manage data pipelines using Azure Data Factory to efficiently and securely extract, transform, and load data from various sources
Develop and optimize storage solutions on Microsoft Azure, ensuring data availability, scalability, and reliability
Automate data-related jobs, processes, and workflows to streamline data delivery and processing using Azure services and tools
Collaborate closely with cross-functional teams, including data scientists, analysts, and developers, to understand data requirements and support their data-related needs
Design and maintain data architecture and data models to enable efficient data consumption by business applications and analytical tools
Ensure data quality, integrity, and security throughout the data lifecycle.
Troubleshoot and resolve data-related issues, performance bottlenecks, and system failures promptly
Stay up to date with the latest trends and best practices in data engineering and cloud computing, particularly in the Azure ecosystem
Research new data sources, third-party platforms, and data engineering best practices
Assist Data Scientists and the larger IT team with general analyses and dashboards, as needed
Other duties, as assigned