Senior Analytics Engineer, Enterprise Analytics

The Inland Real Estate Group of Companies•Oak Brook, IL

3d•$126,580 - $164,554

About The Position

Inland is seeking a highly skilled Senior Analytics Engineer to serve as a technical and analytical leader within our Enterprise Analytics team. This role sits at the intersection of business intelligence, data engineering, commercial real estate, and artificial intelligence, helping transform enterprise data into trusted, governed, and AI-ready insights. As a key contributor to Inland's Atlas Data Foundation Program, you will design data pipelines, build semantic models, develop certified datasets, and create enterprise reporting solutions that empower business leaders to make informed decisions. This is an opportunity to help shape Inland's analytics strategy while working with cutting-edge Microsoft and Azure technologies. If you are passionate about data architecture, analytics innovation, and leveraging AI to drive business value, we want to hear from you.

Requirements

Bachelor's degree in Computer Science, Engineering, Mathematics, Physics, Finance, Economics, or a related discipline required.
8+ years of progressive experience in business intelligence, analytics engineering, data engineering, or related disciplines.
4+ years of hands-on Power BI experience, including semantic modeling, advanced DAX, and workspace administration.
3+ years of experience in commercial real estate, investments, private equity, financial services, or related industries.
2+ years of experience developing and maintaining API-based integrations and cloud data pipelines.
Advanced Power BI and data visualization expertise.
Strong SQL and T-SQL development skills.
Dimensional modeling, semantic layer design, and enterprise data warehousing experience.
REST and SOAP API integration development.
Azure Data Factory, Azure Synapse, Azure Storage, ETL/ELT pipeline design, and orchestration.
Data governance, stewardship, quality, and information security practices.
Practical experience with Microsoft Copilot and AI-enabled analytics solutions.

Nice To Haves

Master's degree in Data Science, Analytics, Finance, or a related field preferred.
Microsoft Fabric, OneLake, Lakehouse, Direct Lake, and Fabric administration.
Python for data engineering, analytics, and machine learning.
Snowflake or Databricks experience.
Tableau experience.
Data catalog and governance platforms such as Microsoft Purview or OvalEdge.
Power Automate, Copilot Studio, or workflow automation platforms.
Git, Azure DevOps, and analytics deployment pipelines.
Master data management and entity resolution.
Experience with DealCloud, Yardi, Salesforce, or HubSpot.

Responsibilities

Serve as a lead analytics partner for the Atlas Data Foundation Program, translating governed data domains into trusted reporting assets.
Collaborate with stakeholders across investment management, finance, asset management, property management, leasing, and investor relations to define and measure key business outcomes.
Help shape Inland's Enterprise Analytics roadmap by evaluating data sources, APIs, and emerging technologies.
Participate in architecture reviews, platform decisions, system migrations, and enterprise-wide data initiatives.
Design, develop, and maintain scalable data ingestion pipelines into a centralized enterprise reporting environment.
Build and support integrations with internal applications and third-party data providers through REST and SOAP APIs.
Create and optimize dimensional data models, semantic layers, and enterprise reporting structures.
Develop ETL/ELT processes and ensure data reliability through monitoring, troubleshooting, and performance optimization.
Work extensively within the Microsoft and Azure ecosystem, including Microsoft Fabric, OneLake, Azure Data Factory, Azure Synapse, and Azure Storage technologies.
Design and manage enterprise dashboards and visualizations that drive business insights.
Establish reporting standards and best practices across the organization.
Support self-service analytics through certified datasets, reusable templates, governance controls, and user enablement.
Prepare enterprise data models for AI-driven analytics and natural language querying.
Support and enhance conversational analytics initiatives, including Copilot in Power BI.
Automate recurring reporting processes, investor reporting workflows, and data validation activities.
Explore predictive analytics and machine learning opportunities, including forecasting and revenue optimization models.