Lead Data Engineer / Data Architect (Supply Chain)

Key Responsibilities

Data Architecture & Design

• Design and develop Supply Chain Anomaly Detection and Revenue Assurance platforms for order processing data.
• Define and own the end-to-end data architecture, including ingestion, transformation, storage, and consumption layers.
• Design robust data models for inventory, logistics, fulfillment, and supplier performance domains.
• Establish architecture standards, design patterns, and best practices aligned with enterprise data strategy.

Data Engineering & Platforms

• Architect and guide development of scalable data pipelines using:
  • PySpark / Spark-based processing
  • Python for data transformation and orchestration
  • Enterprise ETL/ELT frameworks
  • Advanced SQL for analytics and modeling
• Support both batch and near real-time processing use cases.
• Optimize pipelines for data quality, performance, scalability, and cost efficiency.

Supply Chain Analytics Enablement

• Enable downstream analytics 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 readiness for advanced analytics.

Cloud & Data Storage

• Design and implement cloud-native data solutions.
• Ensure secure, scalable, and resilient data storage and access patterns.

Data Governance & Quality

• Collaborate with governance and security teams to ensure:
  • Data quality, consistency, and reliability
  • Data lineage, metadata management, and documentation
  • Compliance with data privacy, security, and regulatory standards

Leadership & Collaboration

• Collaborate with product owners, supply chain leaders, engineering teams, and vendors.
• Translate business and operational requirements into scalable technical solutions.
• Mentor and guide data engineers and architects on best practices and design principles.

Qualifications

• Experience: 12+ years in Data Engineering / Data Architecture
• Data Pipelines: Strong experience with Kafka, CDC, ETL/ELT tools
• Streaming: Hands-on experience with Spark Streaming, Kafka Streams
• Programming: Proficiency in Python, PySpark, SQL
• Analytics: Strong SQL and experience building reports, dashboards, and KPIs
• Data Governance: Experience with data lineage tools (e.g., OpenLineage) and compliance frameworks
• Soft Skills: Strong communication and stakeholder management skills
• Education: Bachelor’s degree in Computer Science, Engineering, or equivalent