Supply Chain data Lead
Listed on 2026-09-03
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IT/Tech
Data Engineering, Data Warehousing, Data Analyst, Data Science Manager
Lead Data Architect
Key Responsibilities
Data Architecture & Design
• Responsible in designing Supply Chain Anomaly Detection and Revenue Assurance platform for Order processing data platform.
• Define and own end-to-end supply chain data architecture, including source ingestion, transformation, storage, and consumption layers.
• Design data models for supply chain domains such as inventory, logistics, fulfillment, and supplier performance.
• Establish architecture standards, patterns, and design guidelines aligned with the enterprise data strategy.
Data Engineering & Platforms
• Architect and guide development of scalable data pipelines using PySpark and Spark-based processing, Python for transformation, orchestration, and data services, enterprise ETL/ELT frameworks, and advanced SQL for data modeling and analytics.
• Support both batch and near–real-time data processing use cases.
• Optimize pipelines for data quality, performance, scalability, and cost.
Supply Chain Analytics Enablement
• Enable downstream usage for supply chain planning and forecasting, inventory optimization and demand analytics, vendor and procurement performance reporting, operational KPIs and executive dashboards, and SKU Management.
• Partner with analytics and data science teams to ensure data is fit for advanced analytics platforms.
Cloud & Data Storage
• Design and oversee implementation of data solutions leveraging cloud-native data platforms.
• Ensure secure, compliant, and resilient data storage and access patterns.
Data Governance & Quality
• Partner with governance and security teams to ensure data quality, consistency, and reliability.
• Data lineage, metadata management, and documentation.
• Compliance with data privacy, security, and internal policies.
Leadership & Collaboration
• Collaborate with product owners, supply chain leaders, engineering teams, and vendors.
• Translate business and operational needs into technical architecture solutions.
• Mentor data engineers and architects on best practices and design principles.
Qualifications
• Data Engineering: 10+ years building data pipelines with Kafka/CDC, ETL tooling.
• Streaming Expertise:
Hands on with stream processing using Spark Streaming, Kafka Streams etc.
• SQL & BI:
Strong SQL/analytics skills and experience building dashboards.
• Data Governance:
Familiarity with lineage/audit tools (Open Lineage), data privacy, and regulatory controls.
• Communication:
Strong cross functional collaboration and experience presenting to executives.
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Education:
Bachelor's degree in CS/Engineering, or equivalent practical experience.
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