Supply Chain Data Lead

JD for lead role:

Key Responsibilities

Data Architecture & Design

·       Responsible in designing SupplyChain 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 (OpenLineage), data privacy, and regulatory controls.

·       Communication: Strong cross‑functional collaboration and experience presenting to executives.

·       Education: Bachelor’s degree in CS/Engineering, or equivalent practical experience.