Data Engineer Lead Supply Chain

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
  • 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
  • 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 (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.