BI Data Modeling Engineer

BI Data Modeling Engineer

Salary: $120k-$140k + bonus

Location: Chicago, IL

Hybrid: 3 days onsite, 2 days remote

*We are unable to provide sponsorship for this role*

Qualifications

·        Bachelor’s degree

·        7+ years of experience in data modeling, data warehousing, and ETL processes.

·        Strong Hands-on experience of dimensional modeling, fact tables, star/snowflake schemas, slowly changing dimensions (SCDs), and tools like Erwin.

·        Expertise with SQL (including Transact-SQL) and deep experience with relational database systems such as Microsoft SQL Server.

·        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.

·        Familiarity with Business Intelligence (BI) platforms such as Microsoft Power BI and Qlik Sense, and experience supporting enterprise reporting.

·        Exposure to Python, PySpark, Databricks

·        Understanding of data governance principles, access controls, and data protection

·        Experience working with operational analytics domains such as Human Resources (HR), Finance, or Risk Management; professional services industry experience is a plus.

Responsibilities

·        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.