Role :- Data Architect with strong Retail and AWS Experience
Location :- Plano, TX(Hybrid) (once in a 2 week or occasional basis)
Duration:- Long Term
Job Description :-
Own the data layer of a multi-tenant ecommerce platform running commercially competing retail brands on shared infrastructure. The core challenge is designing data isolation across brands sharing the same platform while meeting PCI-DSS and GDPR compliance requirements for cardholder data and customer PII.
What you''ll do:
- Design and enforce the canonical domain model (Product, Order, Customer, Cart, Inventory, Price, Payment) that all brands conform to
- Design the data pipelines connecting enterprise systems (ERP, OMS, Inventory, CRM, Pricing, payments) to the platform
- Design the data infrastructure to support future AI/ML workloads — tenant-partitioned feature stores, clickstream capture, entity resolution, data lineage, and PII governance
- Lead brand migration. Facilitate workshops with brand teams and upstream system owners to map existing data to the canonical model, identify gaps, and agree the migration path & data migration automation
- Present data architecture decisions as formal ADRs to the Architecture Review Board
- Partner with the Platform Architecture Lead as the data domain expert — drive the data workstream independently
Tech stack:
AWS, AWS (RDS PostgreSQL, ElastiCache, S3, EventBridge, Glue, DMS), Datastax Astra, Neptune, SageMaker Feature Store, EMR, Snowflake
What you bring:
- Multi-tenant data isolation at scale
- Event-driven architecture and change data capture pipeline engineering
- Ecommerce domain modeling across order management, inventory, and pricing
- Snowflake or equivalent data warehouse/lakehouse technologies . schema design, partitioning strategies, and query optimization
- PCI-DSS and GDPR data compliance . classification, encryption, access controls, and audit trails for cardholder data and customer PII
- Java and Python proficiency
- AI/ML foundational knowledge . understanding of feature engineering, data pipelines for model training, and how data architecture decisions enable or constrain ML workloads
• Experience driving cross-team alignment and architecture decisions through formal review
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