Sr. Data Architect – Supply Chain & Data Engineering

Pull Logic is transforming the supply chain with a revolutionary availability-oriented paradigm that connects customer demand, inventory, supply, and operational decisions. Backed by cutting-edge research from Georgia Tech's School of Industrial and Systems Engineering, our AI-driven SaaS platform helps manufacturers, distributors, and retailers improve product availability, optimize inventory, strengthen supply-chain planning, and make better operational decisions.

We're looking for a  Sr. Data Architect – Supply Chain & Data Engineering  to join our core technology team. This is a senior, hands-on role responsible for defining and evolving Pull Logic's enterprise data architecture, building scalable data models and pipelines, and translating complex customer data into trusted, standardized supply-chain datasets.

The ideal candidate combines deep data architecture and modeling expertise, strong hands-on data engineering skills, and practical understanding of supply-chain data and business processes.

What You'll Do

Own the architecture, design, and evolution of Pull Logic's enterprise supply-chain data model and data platform architecture.
Understand complex customer data originating from ERP, WMS, CRM, planning, manufacturing, logistics, and other enterprise systems and map it into Pull Logic's canonical data model.
Design and maintain conceptual, logical, and physical data models covering key supply-chain domains including: products and product hierarchies, customers, locations, suppliers and sourcing relationships, inventory and inventory movements, sales and demand history, sales orders and purchase orders, shipments, receipts, and transfers, forecasts and planning data, production and assembly data, lead times and supplier performance, Bills of Material (BOM), and supply-chain network relationships.
Define and evolve a canonical supply-chain data model that enables Pull Logic applications, optimization engines, analytics, and AI agents to operate consistently across customers and industries.
Establish standardized customer-to-Pull Logic source-to-target mappings, transformation specifications, semantic definitions, and reusable onboarding patterns.
Architect and build scalable data ingestion and transformation pipelines for batch files, APIs, databases, object storage, and enterprise systems.
Design and govern Bronze / Silver / Gold data architecture, ensuring clear separation between raw customer data, standardized canonical data, and analytics/AI-ready datasets.
Define and implement data contracts, including schema definitions, grain, keys, required attributes, refresh frequency, source lineage, business rules, and data-quality expectations.
Build reusable frameworks for data ingestion, schema validation, data transformation, data enrichment, incremental processing, data reconciliation, error handling and replay, and data quality monitoring.
Establish robust data quality frameworks covering schema validation, completeness, referential integrity, business-rule validation, statistical anomalies, data freshness, and reconciliation.
Design scalable data structures optimized for large-scale supply-chain analytics, forecasting, simulation, optimization, and AI workloads.
Define appropriate table structures, partitioning strategies, incremental-load approaches, indexing, and performance optimization techniques.
Drive adoption of modern Lakehouse and open-table architectures, including Apache Iceberg and cloud object storage.
Establish standards for metadata management, data lineage, schema evolution, auditability, versioning, and data governance.
Collaborate closely with Data Science, AI Engineering, Platform Engineering, Product, Customer Engineering, and Customer Success teams to ensure data architecture supports both current product requirements and future platform capabilities.
Partner with customer technical and business teams to understand their systems, source data structures, supply-chain processes, data semantics, and data-quality issues.
Lead technical data-discovery sessions during enterprise customer onboarding and translate business terminology into scalable technical data models.
Identify opportunities to reduce customer-specific engineering through reusable connectors, mappings, canonical transformations, and common data services.
Establish data engineering and modeling best practices including code reviews, testing standards, documentation, naming conventions, observability, and release management.
Mentor Data Engineers and contribute to building a strong data architecture and engineering competency within Pull Logic.
Ensure data architecture and engineering practices align with enterprise security, governance, and SOC 2 expectations.

What We're Looking For

10+ years of overall experience  in Data Architecture, Data Modeling, Data Engineering, Data Platforms, or related disciplines.
Minimum 5+ years of experience  in senior Data Architecture / Data Modeling roles, preferably supporting large-scale enterprise…