Enterprise Cloud Data Architect

Panera Bread•Saint Louis, LA

32d•$188,838 - $226,606•Hybrid

About The Position

We are seeking a strategic, forward-thinking Enterprise Cloud Data Architect to define, design, and govern the next-generation enterprise data ecosystem. The role sets the enterprise cloud data strategy, reference architecture, governance frameworks, and advanced AI architecture on Google Cloud Platform (GCP). It champions a “Data as a Product” mindset and ensures data assets are secure, scalable, high-quality, and aligned with business objectives. The position is based in St. Louis, Missouri, or Newton, Massachusetts, in a hybrid work arrangement. It is an individual-contributor architecture role with no direct reports, while mentoring engineering and project teams. The listed annual pay range is $188,838–$226,606; actual compensation depends on factors including relevant experience, job-related knowledge, skills, and geographical location.

Requirements

Bachelor’s degree in computer science, engineering, MIS, or a related field.
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, including BigQuery, Cloud Storage, Cloud SQL, Datastore, Pub/Sub, DataProc, and Managed Apache Spark.
Understanding of cloud infrastructure, networking, security governance (including IAM and Admin roles, Cloud Security, VPC Networks, and Firewalls), cloud billing and 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 or GitHub, code versioning, Managed Airflow Composer, other database systems such as Oracle and Postgres, and Kafka/Pub/Sub event-driven architecture.
Programming proficiency in Python, Spark, PySpark, and Pandas for data processing.
Regularly able to sit, stand, talk, hear, and use hands and fingers to operate keyboards and devices.

Nice To Haves

Master’s degree preferred.

Responsibilities

Shape enterprise data strategy and define the enterprise data reference architecture; lead technical design and own and manage reusable design patterns.
Develop data management best practices and frameworks, translating enterprise needs and innovation ideas into scalable enterprise data solution architectures.
Serve as the expert on data ingestion into the Enterprise Data Lake and Medallion Architecture.
Define and maintain enterprise conceptual, logical, and physical data models; establish taxonomy, canonical data definitions, metadata standards, and a semantic layer.
Architect storage and modeling structures for cloud data warehouses, transactional stores, and AI readiness; apply Domain-Driven Design and context layers to isolate domain ownership.
Apply normalization and denormalization, dimensional modeling, Data Vault 2.0, and Medallion Architectures; integrate models with lineage tools, data catalogs, and data quality checks.
Own enterprise data governance policies and data quality stewardship; drive data protection frameworks, including DLP implementation in GCP BigQuery.
Define data discovery, classification, and cataloging strategies, including labeling data by sensitivity so cloud systems apply appropriate security controls, access limits, and retention rules.
Promote a “Data as a Product” mindset and support enterprise data readiness for agentic AI and generative AI use cases and data democratization.
Architect AI foundations using Google Gemini Enterprise Agent Platform and agentic AI tools, including Agent Studio, Model Garden, ADK, and MCP Servers; support scaling machine learning, generative AI, and Vertex AI workloads.
Partner with Enterprise Security, Compliance, and DevOps teams to establish IAM, VPC networking, firewall standards, and integration patterns using ServiceNow workflows.
Mentor engineering teams on architectural standards, design patterns, and frameworks.
Implement proof-of-concept and pilot ideas as working prototypes and repeatable enterprise-grade design frameworks.