Enterprise Cloud Data Architect

Panera Bread•Saint Louis, LA

2d•$188,838 - $226,606•Hybrid

About The Position

We are seeking a strategic and forward-thinking Enterprise Cloud Data Architect to define, design, and govern our next-generation enterprise data ecosystem. In this role, you will be the chief visionary for our cloud data strategy, setting the enterprise data reference architecture, governance frameworks, and advanced AI architecture on Google Cloud Platform (GCP). You will champion a "Data as a Product" mindset, ensuring our data assets are secure, scalable, high-quality, & aligned with business objectives.

Requirements

Bachelor’s degree in computer science, Engineering, MIS, or related field (Master’s preferred).
10+ years of relevant experience in enterprise data architecture, data modeling, and cloud strategy in large-scale data environments.
Comprehensive understanding of GCP core services (BigQuery, Cloud Storage, Cloud SQL, Datastore, Pub/Sub, DataProc, Managed Apache Spark), Cloud infrastructure, networking, security governance (IAM & Admin role, Cloud Security, VPC Networks, Firewalls), Cloud Billing & Resource Usage, Observability and Logging, Workload management and Scaling
Experience managing Cloud Functions, Cloud Run, Compute Engine, and App Engine; working knowledge of DataPlex (Knowledge Catalog).
Experience with CI/CD and Terraform Cloud deployments, GitLab/GitHub, Code Versioning, Managed Airflow Composer, other database systems (Oracle, Postgres), and Kafka/Pub/Sub event-driven architecture.
Programming proficiency in Python, Spark, PySpark, and Pandas for data processing.

Responsibilities

Shape the enterprise Data Strategy and define the Enterprise Data Reference Architecture; lead technical design, own and manage reusable design patterns, and apply advanced systems thinking across the data landscape.
Spearhead the development of data management best practices and frameworks; translating complex enterprise needs and innovation ideas into scalable Enterprise Data Solution Architectures.
Serve as the expert on data ingestion into Enterprise DataLake and Medallion Architecture.
Define and maintain the enterprise Conceptual, Logical, and Physical Data Models, ensuring alignment across disparate business units; establish universal taxonomy, canonical data definitions, metadata standards, and a semantic layer to eliminate data silos and support AI readiness.
Architect flexible storage and modeling structures optimized for Cloud data warehouses (i.e. BigQuery), transactional stores, and AI readiness (vector & semantic stores); apply Domain-Driven Design (DDD) and Context layer to isolate domain ownership in modern distributed architectures.
Apply normalization/denormalization techniques, Dimensional Modeling (Star/Snowflake schemas), Data Vault 2.0, and Medallion Architectures; integrate models with data lineage tools, active data catalogs, and data quality checks so changes are tracked and impact analysis is automated.
Own enterprise Data Governance policies and champion a strict Data Quality steward mindset; drive data protection frameworks, including DLP implementation in GCP BigQuery.
Own data discovery, classification, and cataloging strategies for labelling enterprise data (public, internal, confidential, restricted) so cloud systems apply the correct security controls, access limits, and retention rules; advocate and build an organizational "Data as a Product" mindset.
Architect modern AI foundations, specializing in Google Gemini Enterprise Agent Platform and agentic AI design and development using Agent Studio, Model Garden, Agent Development Kit (ADK), and MCP Servers for integration; support enterprise data readiness for Agentic AI and Generative AI use cases and data democratization; apply a deep background scaling machine learning, Generative AI, and Vertex AI workloads in enterprise environments.
Partner with Enterprise Security, Compliance, and DevOps teams to establish IAM, VPC networking, firewall standards, and integration patterns (leveraging ServiceNow workflows).
Mentor engineering teams on architectural standards, design patterns and frameworks.
Implement proof-of-concept (PoC) and pilot ideas into working prototypes and enterprise-grade design frameworks for repeatable use.