Business Intelligence Data Modeler Engineer

NO SPONSORSHIP - NO OPT

Business Intelligence Data Modeler Engineer

Location: Chicago, IL hybrid still 3 days onsite

• Hard requirements (the candidate needs to have most of these, especially the first 4 listed)
  • Data modeling
  • Microsoft technology stack
  • SQL Server
  • Microsoft Azure
  • Azure Synapse Analytics
  • Microsoft Data analytics foundation

Looking for a candidate with 7 years Data Modeling, Data Warehousing and ETL dimensional modeling.

• 
  Data Modeling Design: Create and maintain conceptual, logical, and physical data models that support reporting and analytics across enterprise systems.
  Business Partnership: Collaborate with stakeholders to translate business needs into scalable data structures and solutions.
  ETL Development Support: Contribute to the design of Extract, Transform, Load (ETL) processes to ensure efficient data movement into Business Intelligence (BI) platforms and warehouses.
  Data Quality & Validation: Implement checks and rules to ensure data accuracy, consistency, and reliability across systems.
  Performance Optimization: Continuously monitor and refine data models and database performance for efficient querying and reporting.
  Documentation & Governance: Maintain clear documentation of models, schemas, and data flows while adhering to data governance standards.
  Data Security: Help design access controls and security measures to protect sensitive data.
  Advanced Querying: Develop and utilize Structured Query Language (SQL) queries for ad hoc analysis, troubleshooting, and data validation.
  Agile Collaboration: Participate in Agile workflows across analysis, development, quality assurance (QA), and user acceptance testing (UAT) phases as a data subject matter expert.
  Domain Expertise: Build strong knowledge of business systems and data domains to support strategic initiatives.
  Continuous Learning: Stay current with evolving technologies and industry trends in data modeling and analytics.

QUALIFICATIONS:

Bachelor s degree in computer science, a related field, or equivalent practical experience.
Experience: 7+ years of experience in data modeling, data warehousing, and ETL processes.

Required
Data Modeling Expertise: Strong Hands-on experience of dimensional modeling, fact tables, star/snowflake schemas, slowly changing dimensions (SCDs), and tools like Erwin.
Technical Skills: Expertise with SQL (including Transact-SQL) and deep experience with relational database systems such as Microsoft SQL Server.
Data Engineering Knowledge: Experience with data pipelines using ETL or Extract, Load, Transform (ELT) techniques and tools such as Azure Data Factory (ADF) to load data into analytics data marts, data warehouses and data lakes. Experience with analytics data platforms such as Azure Synapse Analytics, OneLake or Microsoft Fabric.
Analytics & BI Tools: Familiarity with Business Intelligence (BI) platforms such as Microsoft Power BI and Qlik Sense, and experience supporting enterprise reporting.
Programming & Scripting: Exposure to Python, PySpark, Databricks, or programming languages like Java or C# is a plus.
Data Governance & Security: Understanding of data governance principles, access controls, and data protection best practices.
Business Acumen: Experience working with operational analytics domains such as Human Resources (HR), Finance, or Risk Management; professional services industry experience is a plus.
Collaboration & Communication: Strong problem-solving, interpersonal, and communication skills with the ability to work cross-functionally.