Senior Data Architect

About the Role

This role involves designing and building an enterprise data platform with scalable ingestion, processing, and governance capabilities. It focuses on enabling AI/ML and data products through secure, high-quality, and reusable data architectures. It also includes driving architecture decisions and mentoring engineering teams

Work Authorization – US Citizenship is required for this position.

Key Responsibilities

• Architect and evolve a multi-layer enterprise data platform spanning ingestion, storage, processing, governance, and AI-ready data product layers 

• Design end-to-end data pipelines supporting batch, near-real-time, API, and streaming ingestion patterns from a broad range of enterprise and external sources 

• Define and enforce data governance frameworks including data classification, data quality standards, lineage tracking, and compliance controls 

• Build and maintain data products and ontologies/knowledge graphs that enable reusable, AI-ready datasets for business domains 

• Collaborate with AI/ML teams to ensure the platform supports LLM, ML model training, and Agentic AI workloads 

• Lead architecture decisions across structured, unstructured, and semi-structured data storage and processing 

• Partner with security teams to embed data classification, access control, and security tooling throughout the platform 

• Drive adoption of platform standards and best practices across engineering, manufacturing, and enterprise business units 

• Evaluate and integrate third-party tools and partner solutions to extend platform capabilities 

• Mentor engineers and serve as a technical authority across cross-functional teams

Required Qualifications

• 7–8+ years of experience in data architecture, data engineering, or a related discipline within large-scale enterprise environments 

• Deep expertise in cloud data platforms (AWS preferred), including data lake / lakehouse architecture patterns 

• Hands-on experience with ETL/ELT frameworks, data pipeline orchestration, and metadata management 

• Strong understanding of data governance, data stewardship, data quality, and compliance principles 

• Experience designing platforms that support AI/ML and analytics workloads at scale 

• Proficiency with multiple data storage paradigms — structured, unstructured, and ... [truncated for excel]