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 platforms.
- Proven experience designing and implementing enterprise canonical data models and reusable data architectures.
- Strong experience understanding customer/source-system data and translating it into standardized business and analytical models.
- Demonstrated ability to work directly with complex enterprise datasets and independently understand their business meaning, relationships, grain, keys, quality issues,



and transformation requirements.
- Strong knowledge of conceptual, logical, and physical data modeling.
- Deep understanding of: relational data modeling, dimensional modeling, canonical data modeling, master and reference data, Slowly Changing Dimensions, event and transactional data modeling, data contracts, and metadata and lineage.
- Strong hands-on expertise with: SQL, Python, data transformation and ETL/ELT frameworks, cloud object storage such as GCS or S3, large-scale relational and analytical databases, API and file-based data integrations, and batch and incremental data processing.
- Experience designing and building production-grade data pipelines with technologies such as Prefect, Airflow, Spark, Dask, dbt, or similar platforms.
- Experience with modern data platforms and lakehouse technologies such as Apache Iceberg, Delta Lake, BigQuery, Apache Doris, or similar analytical platforms.
- Robust understanding of data quality, observability, lineage, reconciliation, schema evolution, and pipeline reliability.
- Experience designing data architectures for high-volume and high-scale analytical workloads.
- Strong understanding of cloud data architecture, with a preference for experience on Google Cloud Platform (GCP).
- Ability to remain hands-on and build or prototype data pipelines, transformations, and models when required.
- Strong preference for candidates with hands-on experience working with Supply Chain, Manufacturing, Distribution, Retail, or Planning data.
- Experience understanding data from enterprise platforms such as SAP, Oracle, Microsoft Dynamics, Infor, Epicor, Salesforce, Blue Yonder, Kinaxis, o9, Manhattan, or similar systems is highly desirable.
- Experience working with manufacturing, distribution, or retail data models and translating ERP terminology into standardized analytical models is strongly preferred.
- Experience building data platforms that support Machine Learning, optimization, simulation, forecasting, or Agentic AI systems.
- Familiarity with Knowledge Graphs, semantic models, ontologies, or graph databases.
- Experience building multi-tenant SaaS data platforms.

Why Pull Logic?

- Be part of a mission-driven company redefining supply chain intelligence for the retail world.
- Own and shape the data architecture behind enterprise-scale supply-chain planning, optimization, analytics, and AI products.
- Work on cutting-edge technology with real-world impact and high visibility.
- Work at the intersection of Data Architecture, Supply Chain, Optimization, Machine Learning, and Agentic AI.
- Collaborate with researchers and engineers from Georgia Tech, one of the top engineering schools in the world.
- Grow with a dynamic, fast-paced team and play a key role in building the future of our platform.
- Performance-based ESOP opportunities for exceptional contributors who help drive Pull Logic's success and growth.

📌 Sr. Data Architect – Supply Chain & Data Engineering (India)
🏢 Pull Logic
📍 India