Senior Business Intelligence & Data Analyst

County Materials Family of Companies•Westmont, IL

2d•Onsite

About The Position

The Senior Business Intelligence & Data Analyst transforms financial and operational data into accurate, governed, and actionable business intelligence. This role is responsible for contributing to the development, management, and improvement of data solutions that support financial reporting, operational analytics, forecasting, KPI measurement, and executive dashboards. The position will serve as a technical and analytical expert for data modeling, advanced analytics, and reporting within the Microsoft data platform. In collaboration with the Corporate IT Data Engineering Group, this position will assist with governance and standardization of data throughout the enterprise.

Requirements

Bachelor’s degree in Data Science, Analytics, Statistics, Mathematics, or a related discipline.
5+ years of experience in data science, business intelligence, analytics, enterprise data management, or a related function required
3+ years of hands-on experience developing dashboards, semantic models, reports, measures, and enterprise reporting solutions required.
Demonstrated experience working with financial and operational data, including reporting, KPI development, forecasting, or performance analysis.
Demonstrated experience designing, building, and maintaining data models, pipelines, datasets, and analytical repositories.
Demonstrated experience collaborating directly with business stakeholders to define requirements, validate data, and present analytical findings.
Strong SQL and data modeling capabilities, including relational and dimensional modeling concepts.
Working knowledge of Microsoft Fabric, Azure Synapse Analytics, Azure Data Factory, Azure SQL Database, SQL Server, Azure Data Lake Storage, and Microsoft Dataverse.
Advanced Power BI skills including DAX, Power Query, semantic model design, report optimization, Power BI workspaces, refresh management, and governance practices.
Ability to build, troubleshoot, and optimize data pipelines, transformations, and reporting datasets.
Understanding of data security, access control, data quality, and governance practices in a Microsoft environment.
Strong curiosity and passion for using data to improve business outcomes.
High attention to detail and commitment to data accuracy, usability, security, and trust.
Ability to simplify complex technical concepts for non-technical stakeholders.
Self-directed, organized, and comfortable managing multiple priorities in a fast-paced environment.

Nice To Haves

Master’s degree preferred.
8+ years of progressive experience in enterprise data management, analytics, business intelligence, or data science preferred.
Supporting manufacturing, construction materials, logistics, supply chain, field operations, or multi-site industrial environments.
Integrating ERP systems such as Infor or similar enterprise platforms with modern analytical environments preferred.
Developing near-real-time analytics, operational scorecards, or executive performance dashboards preferred.
Leading or contributing to data modernization, reporting standardization, or enterprise analytics transformation initiatives preferred.
Implementing data catalog, classification, governance, or lineage capabilities.
Microsoft certifications such as Fabric Analytics Engineer Associate, Azure Data Engineer Associate, Power BI Data Analyst Associate, Azure AI Engineer Associate, or Azure Solutions Architect Expert preferred.
Certifications or formal training in data governance, AI, machine learning, statistics, financial analytics, or business intelligence preferred.

Responsibilities

Assist Data Engineering team in design and maintenance of scalable data architectures supporting financial and operational analytics.
Develop enterprise data models based on Microsoft Fabric, Azure Synapse Analytics, Azure SQL, SQL Server, Microsoft Dataverse, and Azure Data Lake Storage technologies.
Define and apply standards for data quality, data validation, metadata, lineage, stewardship, and governance.
Partner with business leaders to identify opportunities where data can improve decision-making, cost control, productivity, and business performance.
Build, monitor, and maintain data pipelines using Microsoft Fabric, Azure Data Factory, Synapse Pipelines, Power Query, Power Platform, and related tools.
Integrate data from ERP, finance, operations, manufacturing, banking, CRM, and third-party systems.
Develop ETL/ELT processes that ensure accurate, timely, and repeatable data movement across source systems and analytical repositories.
Optimize data storage and retrieval performance for reporting, analytics, and self-service consumption.
Perform statistical analysis, trend analysis, root-cause analysis, and scenario modeling for business stakeholders.
Develop predictive and prescriptive analytics supporting revenue forecasting, cost analysis, inventory optimization, production planning, and operational performance.
Create standardized KPIs, metrics definitions, and analytical frameworks for Finance and Operations.
Translate analytical findings into clear recommendations, business impact statements, and decision-ready insights.
Collaborate with Power BI developers to design, develop, publish, and support Power BI dashboards, reports, semantic models, scorecards, and executive views.
Build certified datasets and reusable reporting models that support consistent reporting across business units.
Write, optimize, and troubleshoot DAX, Power Query, SQL, and data model logic.
Enable secure self-service analytics while maintaining data governance, report accuracy, and performance standards.
Work with Cybersecurity team to implement secure data access practices aligned with Microsoft Entra ID, role-based access control, sensitivity labeling, and organizational security standards.
Support Microsoft Purview or similar governance capabilities for cataloging, classification, lineage, and compliance.
Collaborate with Finance, Operations, IT, and executive stakeholders to gather requirements, validate outputs, and drive adoption.
Document data models, report logic, refresh schedules, data definitions, and operational support processes.