Supply Chain data Lead

Lead Data Architect

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 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, and 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.