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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Modeling BI Engineer - **Company:** REQUEST TECHNOLOGY - **Location:** Chicago, IL, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Agile Methodology, Data Analysis, Business Systems, C Sharp (Programming Language), Data Validation, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Security, Data Structures, Data Warehousing, Relational Databases, Dimensional Modeling, Python (Programming Language), Microsoft SQL Server, Performance Tuning, Power BI, SQL Databases, Data Streaming, Transact-SQL, Qliksense, Scripting, Azure Data Factory, Database Performance, Microsoft Fabric, Data Lakes, Pyspark, Information Technology, Data Analytics, Physical Data Models, Azure Synapse Analytics, Data Pipelines, Databricks, Programming Languages - **Published:** September 10, 2026 - **Apply:** https://www.dice.com/job-detail/48f9c4af-2183-4673-b465-314843717e79 ## About the Role * Education: 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. * 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. ## Description Prestigious Global Firm is currently seeking a Business Intelligence Engineer with strong Data Modeling experience.. Candidate will play a critical role in shaping how data is structured, accessed, and for enterprise business intelligence. Candidate will be partnering closely with business analysts, data engineers, and business stakeholders, you'll design and support development of data models that power reporting, analytics., * 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. ## Related Videos - [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [JavaScript? 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