The Data Transformation and Management Specialist is responsible for designing, developing, and maintaining scalable data solutions that enable efficient data integration, transformation, and analytics across the organization. This role combines data engineering expertise with supply chain analytics to support business decision-making, improve operational performance, and drive continuous improvement initiatives. The specialist ensures the integrity, availability, and quality of enterprise data while collaborating with cross-functional teams to deliver reliable data pipelines, reporting solutions, and cloud-based data platforms.
Key Responsibilities
Design, develop, and maintain scalable data pipelines to acquire, transform, and load data from multiple enterprise systems using batch and real-time processing methods.
Build, optimize, and maintain ETL processes to ensure reliable, automated, and efficient movement of data across systems.
Develop and maintain database solutions, ensuring optimal performance, scalability, and data availability.
Clean, validate, transform, and enrich raw data to improve quality, consistency, and usability for reporting and analytics.
Monitor data pipelines, databases, and cloud infrastructure to identify and resolve performance issues, data quality concerns, and system bottlenecks.
Utilize cloud technologies, particularly Microsoft Azure services, to manage data storage, processing, and integration.
Provide analytical support for supply chain and business functions through data modeling, reporting, and KPI analysis.
Analyze supply chain performance metrics to identify trends, root causes, improvement opportunities, and actionable recommendations.
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Support inventory optimization, material planning, and planning parameter refinement through data-driven insights.
Participate in continuous improvement initiatives, including Six Sigma and supply chain optimization projects.
Ensure data integrity, governance, and compliance across reporting, analytics, and enterprise data solutions.
Collaborate with data scientists, software engineers, business analysts, and functional stakeholders to understand data requirements and deliver business solutions.
Develop and maintain documentation for data architecture, ETL processes, data models, and system configurations.
Support the development of repeatable analytics and reporting solutions using enterprise business systems and Business Intelligence tools.
Experience
Minimum 4 years of experience in Data Engineering, Data Integration, Data Management, or a related field.
Experience developing and maintaining enterprise-scale ETL/ELT pipelines.
Experience working with cloud-based data platforms, preferably Microsoft Azure.
Experience designing, optimizing, and managing relational databases.
Experience supporting analytics, reporting, and business intelligence solutions.
Experience working with supply chain, manufacturing, or enterprise business data is preferred.
Experience collaborating with cross-functional teams in Agile or project-based environments.
Experience with data quality, monitoring, troubleshooting, and performance optimization.
Exposure to AI/ML data preparation and cloud analytics platforms is an advantage.
Technical Skills
Microsoft Azure (Azure Data Factory, Azure Databricks, Azure Data Lake Storage, Azure SQL Database)
ETL/ELT development using Databricks PySpark and Matillion
Data Transformation, Data Cleansing, and Data Quality Management
SQL and Advanced Query Optimization
Cloud Data Architecture and Data Integration
Business Intelligence and Reporting
Supply Chain Analytics and KPI Reporting
Inventory Optimization and Material Planning Analytics
Documentation and Technical Process Management
Good to Have
Power BI
Python
Scala
Streamlit
AI/ML concepts using Databricks or Snowflake
Neo4j / Graph Database
Advanced analytics and visualization tools
Core Competencies
Communicates effectively with technical and business stakeholders.
Drives results while managing competing priorities and deadlines.
Demonstrates a global perspective and supports enterprise-wide solutions.
Solves complex business and technical problems using analytical thinking.
Optimizes work processes through automation and continuous improvement.
Applies strong analytical and troubleshooting skills to data and supply chain challenges.
Supports inventory management, material planning, and supply chain optimization through data-driven decision making.
Manages and interprets KPIs to identify business improvement opportunities.
Values diverse perspectives and collaborates effectively across global teams.
Maintains a strong focus on data accuracy, governance, and operational excellence.
Qualifications
College, university, or equivalent Bachelor's degree in Computer Science, Information Technology, Data Engineering, Information Systems, Engineering, or a related discipline required.
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