Senior Data Analyst

About the Company

ArcelorMittal's equal opportunity statement is a reflection of their commitment to creating a safe and inclusive workplace where everyone feels welcomed, valued, respected, and heard. The company's journey to build a diverse and inclusive workplace is guided by their longstanding belief in Our Strength is People®. They are focused on enhancing their Diversity and Inclusion commitment with a strong sense of purpose and resolve to evolve into a more diverse and inclusive organization. ArcelorMittal's commitment to diversity and inclusion extends to all areas of their business, including recruitment, job assignment, talent development, skills enhancement, employee retention, policies, and procedures. They strive to create an environment where everyone can bring their whole self to work, where they can excel personally and professionally.

About the Role

We are looking for a highly analytical and detail-oriented Senior Data Analyst to support enterprise-wide data delivery, validation, and business intelligence initiatives. The role focuses on ensuring data accuracy, consistency, completeness, and usability across multiple enterprise systems while supporting business users with trusted, high-quality data for operational and strategic decision-making. The ideal candidate will possess strong expertise in SQL, SSAS, Power BI, DAX, MDX, Power Query, data validation, reconciliation processes, and modern analytics platforms such as Databricks. The role requires working closely with business stakeholders, Data Engineers, Solution Architects, and application teams to analyze complex data flows, identify discrepancies, perform root cause analysis, and continuously improve data quality and reporting processes. This position is ideal for professionals who enjoy solving complex data problems, translating business requirements into analytical solutions, and driving enterprise data quality initiatives.

Responsibilities

• Data Analysis & Validation
• Analyze enterprise data across multiple operational and analytical systems to ensure accuracy, consistency, and completeness.
• Investigate data discrepancies by comparing source systems, transformation layers, semantic models, and reporting outputs.
• Perform end-to-end data validation across ETL pipelines, SQL databases, SSAS cubes, Tabular Models, and modern Lakehouse environments.
• Validate business rules, calculations, KPIs, and metrics used in reports and dashboards.
• Perform data reconciliation between legacy and modern data platforms during migration projects.
• Develop reusable validation scripts and reconciliation frameworks to improve data reliability.
• Ensure data quality standards are consistently maintained across enterprise reporting solutions.
• Business Data Analysis
• Collaborate with business stakeholders to understand reporting requirements and analytical needs.
• Translate business requirements into data validation logic and analytical solutions.
• Conduct impact analysis for data model changes, system enhancements, and business process modifications.
• Support business users in interpreting reports, dashboards, KPIs, and data trends.
• Assist business teams during User Acceptance Testing (UAT) and business validation activities.
• Document business rules, data definitions, transformation logic, and analytical assumptions.
• Data Quality & Root Cause Analysis
• Perform detailed root cause analysis for data inconsistencies, missing records, transformation failures, and reporting anomalies.
• Identify data quality issues across source systems, integration layers, semantic models, and reporting platforms.
• Recommend corrective actions and preventive measures to improve overall data quality.
• Develop monitoring frameworks for proactive identification of data issues.
• Collaborate with Data Engineering teams to resolve ETL and pipeline-related issues.
• Reporting & Analytics
• Develop and maintain analytical datasets to support business reporting.
• Create meaningful dashboards and reports using Power BI.
• Optimize DAX measures and Power Query transformations for improved report performance.
• Support SSAS Multidimensional and Tabular Models used in enterprise reporting.
• Validate calculations within semantic models and reporting layers.
• Improve reporting efficiency through automation and reusable analytical components.
• Data Platform Support
• Investigate SQL Agent job executions and analyze job outputs.
• Monitor scheduled data loads and validate successful data refreshes.
• Support migration from traditional SSAS cubes and MDX reporting to modern Power BI and Tabular Models.
• Assist Data Engineering teams in validating Databricks-based data pipelines.
• Build validation datasets using Python within Databricks environments.
• Understand enterprise data architecture, data lineage, and transformation logic across systems.
• Continuous Improvement
• Recommend improvements to data validation frameworks and reconciliation processes.
• Automate repetitive validation activities wherever possible.
• Participate in modernization initiatives involving Azure Data Platform and Lakehouse architecture.
• Promote data governance, standardization, and best practices.
• Contribute to knowledge sharing and mentor junior analysts.

Qualifications

• Bachelor's degree in computer science, Information Technology, Engineering, Mathematics, Statistics, or a related discipline.
• Microsoft Data Analytics, Power BI, or Azure certifications are desirable.

Required Skills

• Data Analysis
• Advanced SQL (T-SQL)
• Data Validation
• Data Reconciliation
• Root Cause Analysis
• Data Profiling
• Data Quality Assessment
• Business Data Analysis
• Data Lineage
• Data Mapping
• Microsoft Analytics Stack
• SQL Server
• SSAS (Multidimensional & Tabular)
• MDX
• DAX
• Power Query
• Power BI
• Excel (Advanced)
• Modern Data Platform
• Databricks
• Python (Basic to Intermediate)