Principal Enterprise Data Platform Architect

Job description

About the role As the Principal Enterprise Data Platform Architect, you will serve as the technical lead and pillar head for Data Platform Engineering within the Data Excellence & AI Foundations organization. Reporting directly to the Director of Data Excellence & AI Foundations, you will be responsible for defining, architecting, and evolving Avery Dennison’s enterprise 10-Layer Data & AI Operating System. In this role, you will lead the technical transition from legacy ETL pipelines toward a modern, high-code Platform Engineering practice grounded in GitOps, Data-as-Code, declarative management, and automated metadata control planes while mentoring and elevating internal engineering talent.

Key Responsibilities

Position Location: Remote Own the global architecture blueprint and technology decision trees for Avery Dennison's enterprise analytical and AI platforms, architecting the 10-Layer Modern Data Operating System across multi-cloud environments (OCI, GCP, Azure). Architect and deploy the centralized Metadata Control Plane and automated Data Catalog, coordinating schema tracking, lineage graphs, data quality observability, GitOps methodologies, reusable developer toolboxes, and context-as-code for LLM reasoning.

Design and govern enterprise AI infrastructure standards, including Vector Databases, Knowledge Graphs, Feature Stores, and RAG pipelines for corporate Gemini LLM solutions and autonomous AI agents with deterministic verification mechanisms. Design and enforce the Universal Security Model incorporating Attribute-Based Access Control (ABAC), fine-grained masking, and Okta/Active Directory identity integration, while implementing SRE-style observability for platform health, latency, query performance, and compute costs.

Serve as Pillar Lead for the Data Platform Engineering squad, guiding and mentoring technical talent across pipeline, data, analytics, AI/LLM, DevOps, and APEX engineering while partnering with HR on technical career progression tracks and competency matrices.

Qualifications

Qualifications

Master’s or Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, or a related quantitative field with 10+ years in enterprise data architecture, platform engineering, or cloud infrastructure design (including 3+ years leading technical teams in a global enterprise context). Expert technical proficiency in multi-cloud environments (OCI, GCP, Azure), Oracle ADW (23c/26ai), Databricks, and Apache Iceberg or Delta Lake table formats.

High-code data engineering mastery in Python, SQL, Apache Airflow, Kafka/Confluent streaming, Change Data Capture (CDC / GoldenGate), Docker, Kubernetes, Terraform, and GitOps CI/CD deployment pipelines (GitHub Actions/GitLab CI). Direct experience architecting enterprise AI/ML infrastructure, including Vector DBs, Knowledge Graphs, Feature Stores, RAG pipelines, and enterprise LLM integrations (Gemini). Strong strategic leadership and communication skills with a proven track record of mentoring technical talent, establishing Universal Security Models (ABAC, RLS, Okta/AD), and presenting complex architectural blueprints to executive sponsors (VP/CIO).