Senior Data Analyst SCM

Responsibilities
In brief:

1. Collaborate with supply chain leadership to understand reporting and analytical needs, ensuring alignment with business goals.
2. Build and maintain supply chain data marts by integrating data from ERP, Workforce management system, and sales POS, working cross-functionally with source system owners.
3. Design and develop Power BI reports, partnering with the supply chain and operations teams to drive adoption and improve reporting efficiency.

1. Work closely with supply chain, procurement, and operations leadership to identify key reporting needs, translating business requirements into comprehensive data models and analytical solutions.
2. Build and maintain supply chain data marts, establish data pipelines, troubleshoot discrepancies, and ensure seamless integration of data from systems such as ERP, Workforce management system, and sales POS and inventory management platforms. Ensure the accuracy, consistency, and timely availability of supply chain data.
3. Collaborate with cross-functional teams, including procurement, logistics, inventory management, digital transformation, cost control, and operations, to support the company’s supply chain performance and ensure accurate, efficient reporting.
4. Design, build, and maintain Power BI dashboards to provide batch and real-time insights into inventory levels, supplier performance, demand forecasting, procurement efficiency, and fulfillment KPIs.
5. Partner with the supply chain team to promote the adoption of reports, providing training and support to enable effective data-driven decision-making.
6. Conduct in-depth analysis of supply chain data, identifying trends, risks, and opportunities to optimize inventory management, reduce costs, improve supplier performance, and enhance logistics efficiency.
7. Support the preparation of demand forecasts, inventory planning, supplier scorecards, and ad-hoc analyses, providing data-driven insights to refine business strategies.
8. Leverage expertise in supply chain processes and ERP systems (such as Microsoft Dynamics or SAP) to optimize the use of operational data and enhance reporting capabilities.
9. Document projects, data sources, and analyses comprehensively, contributing to knowledge sharing and maintaining data governance standards.

1. Bachelor’s degree in Supply Chain Management, Operations Research, Computer Science, Mathematics, Statistics, Economics, or a related field.
2. Minimum of 8 years of experience in analytics and reporting, preferably with a focus on supply chain, logistics, or procurement analytics.
3. Proficiency in SQL, Python, and DBT is essential, along with strong technical skills in data analysis, statistics, and data warehousing.
4. Experience with data visualization tools such as Tableau or Power BI to create compelling dashboards.
5. Strong understanding of supply chain processes, including inventory management, demand forecasting, supplier performance analysis, and logistics optimization.
6. Familiarity with ERP, WMS, and TMS systems, such as Microsoft Dynamics, SAP, Oracle, or similar platforms (added advantage).
7. Experience in the Food and Beverage (FnB) industry is highly desirable.
8. Excellent communication skills to collaborate effectively with supply chain and operations leadership.
9. Attention to detail and a proactive approach to improving supply chain reporting and analytics processes.
10. Strong ability to translate complex data into actionable insights, influencing decision-making across the organization.
11. Demonstrated ability to collaborate with cross-functional teams, manage multiple priorities, and adapt to a fast-paced, dynamic environment.
12. A continuous learning mindset and passion for staying updated on the latest trends and best practices in supply chain analytics.
13. Successful candidates will bring technical expertise, problem-solving skills, creativity, and the ability to work effectively with colleagues from diverse backgrounds.

1. Data Warehouse / Big Data: Snowflake, ADL (Azure Data Lake), and MS Fabric
2. Reporting: Power BI
3. Data Pipelines: Kafka, Python, SQL, Airflow, Airbyte, and DBT
4. ML Ops: Amazon SageMaker
5. Platform: AWS (Amazon Web Services) and Azure
6. Repository: GitHub
7. Analysis: SQL, Python, and Excel
8. Presentation: Power BI, MS Excel, Miro and MS Power Point
9. Documentation: Confluence
10. Sprints and Backlog: Jira