Lead Data Architect

Data Lead ( Supply Chain )

Philadelphia PA onsite

Longterm contract

W2 Contract

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, fulfilment, 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 (Open Lineage), 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.