Supply Chain data Lead

JD for lead role:

      
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,
o PySpark and Spark-based processing
o Python for transformation, orchestration, and data services
o Enterprise ETL/ELT frameworks
o 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,
o Supply chain planning and forecasting
o Inventory optimization and demand analytics
o Vendor and procurement performance reporting
o Operational KPIs and executive dashboards
o 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,
o Data quality, consistency, and reliability
o Data lineage, metadata management, and documentation
o 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.