As Managing Director, Data Platforms, Architecture, & Operations, you will lead a team of data engineers, platform architects, and operations specialists to drive an enterprise data transformation journey. You will be responsible for defining the strategy, architecture, and operational excellence of large-scale data ecosystems while overseeing migrations from legacy systems to modern cloud environments. Your daily work involves designing scalable distributed architectures for advanced analytics and AI-first engineering, managing DataOps and MLOps strategies, and ensuring "Secure By Design" principles across all platforms. You will utilize Google Cloud Platform (GCP) and Databricks to manage high-volume data processing and infrastructure as code. The role focuses on solving complex problems related to cloud modernization, proactive security risk mitigation, and the development of robust data ingestion frameworks within a massive global data footprint.
Define and execute the overarching data and platform architecture strategy aligned with enterprise goals.
Lead large-scale data platform migrations and modernization efforts from legacy systems to GCP.
Design scalable, distributed data architectures to support advanced analytics and AI-first engineering.
Oversee the administration, optimization, and scaling of enterprise cloud environments in GCP and Databricks.
Drive DataOps and MLOps strategies to ensure high availability and performance of critical data pipelines.
Manage cloud resource consumption, optimize costs, and oversee vendor performance and SLA adherence.
Embed "Secure By Design" methodologies and enforce robust data governance across all platforms.
Evaluate and implement best-in-class data engineering tools and frameworks to improve pipeline efficiency.
What we're looking for
Candidates must reside within 50 miles of the Plano, Texas campus.
Bachelor's degree in computer science, math, business, or a related quantitative field is required.
Extensive experience in systems development including design, implementation, and maintenance of large batch or online transaction systems is required.
Experience with mainframe, distributed systems, client server, relational systems, transaction/message systems, embedded systems, imaging, and systems management is required.
Proven hands-on architectural and operational expertise in Google Cloud Platform (GCP) and Databricks is required.
Demonstrated success in managing, scaling, and migrating large-scale cloud data platforms and distributed systems is required.
Deep understanding of cloud security protocols, identity and access management, and "Secure By Design" methodologies is required.
Strong background in modern data engineering stacks, DataOps/MLOps, infrastructure as code (IaC), and CI/CD pipelines is required.
GCP
Databricks
DataOps
MLOps
Infrastructure as Code
CI/CD
Data Engineering
Distributed Systems
Identity and Access Management
Data Governance
Cloud Security
Data Engineering
Data Infrastructure
Database Engineering
Data Governance
Analytics Engineering
Data Quality
Data Architecture
Machine Learning Platform
Data Pipelines
Data Architecture
Data Governance
Master Data Management (MDM)
Snowflake
Databricks
AWS
Azure
GenAI
LLM
Data Engineering
API
Data Catalog
GxP
TOGAF
DAMA CDMP
PMP
TBM Frameworks