Lead Data Architect - Hybrid (Dallas or Houston, TX)

We are seeking an experienced Lead Data Architect to support and drive the overall data architecture strategy for our organization, encompassing a cloud-based data platform, lakehouse, data warehousing, and the data infrastructure required to support our growing AI, advanced analytics, and business intelligence use cases. This critical, hands-on role will be responsible for designing and building scalable, secure, and high-performance data solutions, establishing data standards, and ensuring data quality and governance for the enterprise. The ideal candidate will possess deep technical expertise in data modeling, data integration, and database technologies, coupled with strong leadership and communication skills to collaborate effectively with diverse teams and stakeholders. 

This position will offer flexibility for hybrid work schedules to include both in-office presence and telecommute/virtual work to be based in either Dallas or Houston, TX.

Key Responsibilities: 

Data Architecture Strategy and Vision:

• Develop and maintain the overarching data architecture blueprint, aligning with business strategy, application architecture, and technology roadmaps.
• Define principles, standards, and guidelines for data modeling, data integration, data storage, and data access. 

Data Modeling and Design:

• Lead the design and development of reusable data products that meet the needs of various use cases, including GenAI/AI/ML pipelines, and reporting/BI.
• Ensure data integrity and accessibility. 

Data Integration and ETL/ELT Architecture:

• Design and oversee the implementation of robust and scalable data integration processes, including ETL/ELT pipelines, change data capture, and master data management.
• Ensure data quality, consistency, and timely delivery of data. 

Data Storage and Management:

• Evaluate and select appropriate data storage technologies (e.g., relational databases, NoSQL databases, data lakes, data warehouses, vector database) based on performance, scalability, cost, and security requirements.
• Design tooling and automation to support data governance operations, including data retention policies and data lifecycle management strategies. 

Data Architecture for AI and Advanced Analytics:

• Design and implement the data infrastructure required to support AI/ML initiatives, including feature stores, data pipelines for model training and inference, and access to large-scale datasets.
• Optimize data architecture for the specific needs of advanced analytical techniques. 

Data Architecture for Business Intelligence:

• Ensure N-tier data products are designed and optimized to support efficient and accurate business intelligence reporting and analysis.
• Facilitate self-service BI capabilities through well-structured and governed data assets. 

Data Governance and Quality:

• Collaborate with the Data Governance team to establish and enforce data governance policies, standards, and procedures.
• Design data quality frameworks, monitoring processes, and remediation strategies. 

Technology Evaluation and Selection:

• Evaluate emerging data technologies and trends, providing recommendations for adoption based on business needs and architectural fit.
• Conduct proof-of-concepts and pilot projects to validate new technologies. 

Collaboration and Communication:

• Partner closely with data scientists, data engineers, software engineers, business analysts, and other stakeholders to understand data requirements, translate them into architectural designs, and provide technical guidance.
• Effectively communicate complex data concepts to both technical and non-technical audiences. 

Technical Leadership and Mentorship:

• Provide technical leadership and mentorship to the data management team that includes additional data architects, data analysts, data engineers, and other technical resources involved in data-related projects.
• Guide architectural decisions and ensure the quality and consistency of data solutions. 

Performance Monitoring and Optimization:

• Define monitoring strategies for data pipelines and data storage systems to track performance, identify bottlenecks, and optimize resource utilization. 

Security and Compliance:

• Design data architectures that adhere to security policies, compliance requirements (e.g., GDPR, CCPA), and data privacy regulations.