Mid-Level Data Analyst

Perform detailed data analysis to identify anomalies, inconsistencies, and data quality issues in source systems.

• Support data profiling, analysis, and transformation activities across legacy and target systems, including handling data in relational, NoSQL, and object storage environments.
• Develop and maintain two-way data mappings (source-to-target and target-to-source) to ensure data completeness, traceability, and alignment with business requirements.
• Maintain a strong understanding of target data models, database structures, and data usage patterns to support accurate data transformation and validation.
• Contribute to the design, architecture, and management of data lake and data warehouse solutions for structured, performant, and accessible data.
• Collaborate with Business Analysts, Technical Architects, and development teams to translate business rules into data mappings and transformation logic.
• Identify data quality issues and gaps, support data cleansing decisions, and ensure alignment with approved data mapping and transformation guidelines.
• Perform and support data validation and reconciliation activities to confirm data accuracy, completeness, and usability throughout data pipelines and migration processes.
• Document data flows, schemas, mappings, and transformation logic to ensure traceability, auditability, and governance compliance.
• Participate in the continuous improvement of data platform architecture, tooling, and data migration strategies in accordance with organizational standards and best practices., About the Team The Supply Chain team focuses on keeping things moving across all aspects of our business. They make an impact by ensuring our products get from the vendors to the…
• 4 days ago

3+ years of experience in data analysis, development, or related roles involving data integration or migration.

• Experience with data mapping, transformation, and validation in migration/integration contexts.
• Solid understanding of database design principles: relational modeling, normalization, and denormalization.
• Proficiency in SQL, especially with Azure SQL Database, Azure SQL Managed Instance, or similar platforms.

• Ability to move and transform data across structured, semi-structured, and unstructured formats. Nice to Have:

• Experience with MS SQL Server, Azure Cosmos DB, or other NoSQL databases.
• Knowledge of Kusto Query Language (KQL), Azure Monitor, and/or Azure Data Explorer.
• Experience with object storage patterns, such as blob or file-based architectures.
• Familiarity with data lakes and warehouses (e.g., Azure Data Lake Storage, Microsoft Fabric).
• Exposure to data science or advanced analytics platforms (Azure Machine Learning, Databricks, etc.).
• Understanding of data governance, lineage, cataloging, and metadata management.