Director, Enterprise Data Architect

Position Summary:

At Vista, we are redefining how data is leveraged to drive business growth and operationalize AI. We are seeking a hands-on Data Architect who will both lead the design and actively lead the development of our robust data architecture. 

As a Data Architect, you will lead data engineers or cross-functional teams, 

playing a critical role in building and optimizing the data infrastructure that powers real- 

time analytics and advanced business insights. This is a hands-on role where you will be coding, testing, and troubleshooting alongside the team, ensuring high-quality delivery at all levels of the data stack. 

Vista Equity Partners is seeking a highly skilled and experienced Enterprise Data Architect to drive data strategy and modernization across our portfolio companies. This critical role will focus on assessing and enhancing the data estates of our investments, with a particular emphasis on building robust, scalable, and AI-ready data architectures. The ideal candidate will possess deep expertise in all aspects of data engineering, cloud platforms, and data governance, and will play a pivotal role in preparing our portfolio companies for the adoption of advanced technologies, including agentic architectures and machine learning 

Portfolio Company Data Assessment:  

• Conduct comprehensive assessments of portfolio company data estates, identifying strengths, weaknesses, and opportunities for improvement. 

• Evaluate existing data infrastructure, data pipelines, data quality, and data governance practices. 

• Analyze data security and compliance posture, ensuring alignment with industry best practices. 

Data Architecture Strategy & Design:  

• Develop and implement enterprise-level data architectures that support the strategic objectives of portfolio companies, especially for agentic architectures. 

• Design scalable and resilient data solutions leveraging cloud-based platforms (AWS, Azure, GCP). 

• Define data integration strategies, ensuring seamless data flow across disparate systems. 

• Create data models and data dictionaries to standardize data definitions and usage. 

Data Engineering & Implementation:  

• Provide hands-on guidance and support for the implementation of data engineering best practices, including data integration, ETL/ELT, data warehousing, and data lake development. 

• Optimize data pipelines for performance, reliability, and scalability. 

• Implement data quality and validation processes to ensure data accuracy and consistency. 

• Establish data streaming capabilities for real-time data processing. 

AI Readiness & Machine Learning Support:  

• Design and implement data architectures that support machine learning and AI applications. 

• Develop data preparation and feature engineering strategies for machine learning models. 

• Ensure data is accessible and usable for AI-driven insights and decision-making. 

• Prepare data estates for agentic architectures. 

Data Governance & Security:  

• Establish and enforce data governance policies and procedures. 

• Ensure data security and compliance with relevant regulations (e.g., GDPR, CCPA). 

• Implement data access controls and audit trails. 

Technology Evaluation & Selection:  

• Evaluate and select appropriate data technologies, tools, and platforms. 

• Stay abreast of emerging data trends and technologies. 

• Provide guidance on best practices for data related technology. 

Collaboration & Communication:  

• Collaborate with portfolio company technology and business teams to drive data initiatives. 

• Communicate complex technical concepts to non-technical stakeholders. 

• Provide technical leadership and mentorship to portfolio company data teams. 

• Bachelor's or Master's degree in Computer Science, Data Science, or a related field. 

• Minimum of 8+ years of experience as an Enterprise Data Architect or similar role. 

• Deep expertise in data engineering, data warehousing, data lakes, and cloud-based data platforms (AWS, Azure, GCP). 

• Strong understanding of data modeling, data integration, and data governance principles. 

• Hands-on experience with data integration and ETL/ELT tools. 

• Experience with data streaming technologies (e.g., Kafka, Kinesis). 

• Proven ability to design and implement scalable and resilient data architectures. 

• Experience preparing data for machine learning and AI applications. 

• Experience with agentic architectures a strong plus. 

• Excellent communication, presentation, and interpersonal skills. 

• Experience working with portfolio companies, or within a consulting environment is a plus. 

Extensive experience in the following:  

• Data Governance and Security. 

• Machine Learning Data Preparation. 

• Problem-Solving and Analytical Skills.