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
• rchitect 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 dvanced 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.
