Sr Principal Architect, Data & AI

Houston, TX 8020 Katy Fwy
Houston, TX 77024, USA

For more than three decades, Crown Castle has led the way in shared communications infrastructure, delivering profitable solutions by connecting communities, businesses, and people, and enabling each to thrive with reliable access to voice and data in more places, faster than ever before. When you join Crown Castle, you become part of a dynamic team of passionate and collaborative professionals engaging in complex challenges and contributing to projects that shape the future of life and work.

ABOUT THE ROLE

The Senior Principal Architect – Data & AI is the organization’s most senior hands‑on technical authority for modern data platforms, AI, machine learning infrastructure, and cloud‑native data engineering, defining the enterprise technical vision while personally designing, coding, testing, deploying, optimizing, and troubleshooting critical capabilities. Operating from executive strategy through production implementation, and operating at speed, this individual will work across architecture, data engineering and pipelines, machine‑learning systems, infrastructure, software, security controls, and performance engineering, rather than serving primarily in an advisory or governance capacity.

The architect will establish engineering standards and patterns, validate them through working implementations, and lead the most complex technical work across data foundations, data engineering, data models and semantic layers, advanced analytics, AI and ML, automation, and Data and AI governance.

WHAT YOU WILL DO

• Define and evolve the enterprise architecture and technical roadmap for data platforms, AI, machine learning infrastructure, and cloud-native data engineering.
• Serve as the organization’s highest-level hands‑on technical authority, making critical design decisions across data, AI & ML, infrastructure, data integration, security, availability, observability, and deployment.
• Personally design, prototype, code, test, deploy, optimize, and troubleshoot production‑grade data pipelines, infrastructure, and AI and ML capabilities.
• Establish architectural principles, reference architectures, engineering standards, reusable frameworks, and development patterns that improve delivery speed, quality, consistency, and maintainability.
• Translate complex business and scientific problems into secure, scalable, high‑performing technical architectures and working production solutions, in short time frames.
• Evaluate architecture and technology alternatives through research, quantitative analysis, benchmarking, proofs of concept, and production prototypes.
• Define and implement enterprise data architecture across ingestion, transformation, storage, processing, governance, analytics, semantic layers, AI‑ready data, and operational consumption.
• Provide deep technical leadership for Databricks, Apache Spark, lakehouse architecture, PostgreSQL, data modeling, data products, and batch, streaming, event‑driven, and real‑time data pipelines.
• Establish Data Ops practices covering version control, orchestration, automated testing, data quality, metadata, lineage, observability, environment promotion, rollback, and incident response.
• Architect and implement end‑to‑end AI and ML systems spanning data preparation, experimentation, training, evaluation, deployment, inference, monitoring, retraining, and governance.
• Architect and implement secure, scalable Agentic AI systems, including agent frameworks and harnesses, orchestration, tool and data integration, memory and context management, evaluation, observability, governance, human oversight, and controls for reliable production operation.
• Design MLOps capabilities that provide model reproducibility, versioning, lineage, evaluation, explain ability, security, performance monitoring, drift detection, and operational support.
• Architect secure cloud environments and containerized workloads using Kubernetes and Docker, with repeatable infrastructure provisioned through Terraform and Infrastructure as Code.
• Build Git Hub‑based engineering workflows and CI/CD pipelines that automate building, testing, security scanning, packaging,…