Data Scientist/Data Architect/Data Governance Lead

POSITION SUMMARY:
We are seeking a highly skilled and strategic Data Architect to lead our data governance, security, and management initiatives. This senior role will be responsible for designing and implementing the organization's enterprise data architecture, ensuring that our data is secure, reliable, and accessible for business-critical functions. The ideal candidate is a proactive leader who can define data strategy, enforce best practices, and collaborate with cross-functional teams to align our data ecosystem with business goals. 
 
 
 
DUTIES AND RESPONSIBILITIES:
 
Data Governance, Strategy and Architecture

• Define and drive the organization's overall vision, data strategy, roadmap, and architecture vision. This includes the data AI architecture vision, strategy, and roadmap. This includes the design of scalable data lakes, data warehouses, and data fabric architectures.
• Establish and enforce data governance policies and standards to ensure data quality, consistency, and compliance with all relevant regulations (e.g., GDPR, CCPA). Lead the implementation of a comprehensive data governance framework, including data quality management, data lineage tracking, and master data management (MDM). Collaborate with data owners and stewards across business units to establish clear roles, responsibilities, and accountability for data assets. 
• Establish clear rules and policies governing the responsible usage of data within AI and ML models, including documentation of data lineage for model training. Design data infrastructure specifically optimized for AI workloads, including data pipelines for machine learning models, and architect solutions for large language models (LLMs). Develop bias mitigations strategies to ensure diverse and representative datasets to prevent AI biases, and architect monitoring systems for model drift. 
• Evaluate, recommend, and select appropriate data management technologies, including cloud platforms (e.g., AWS, Azure, GCP), storage solutions, and governance tools.
• Architect complex data integration patterns to connect disparate data sources across the organization, ensuring seamless data flow and a unified data view. 

• Design and implement a robust data security architecture to protect sensitive data from unauthorized access, breaches, and corruption.
• Develop security protocols, such as encryption, access controls (IAM), and masking techniques to safeguard data in transit and at rest.
• Conduct regular security audits and vulnerability testing to identify gaps in security architecture and develop remediation plans.
• Ensure the data architecture and its supporting systems are compliant with internal policies and external data protection regulations. 

• Design and maintain conceptual, logical, and physical data models for transactional and analytical systems.
• Oversee the development of database schemas, metadata management, and data cataloging efforts to improve data discoverability and understanding.
• Define and standardize data architecture components, including storage solutions (data lakes, warehouses, etc.), data pipelines, and integration patterns.
• Evaluate and recommend new data technologies, tools, and platforms that align with the organization's strategic needs. 

• Design and implement a robust data security architecture, including controls for access management, encryption, and data masking to protect sensitive information.
• Create and manage an organization-wide data classification scheme based on data sensitivity and importance (e.g., public, internal, confidential, restricted).
• Implement technical controls and processes to automatically classify and tag data assets, ensuring proper handling and security.
• Collaborate with business and legal teams to define and apply data classification rules consistently. 

• Provide technical guidance and mentorship to data engineers, analysts, developers, and other IT teams on best practices for data management and security.
• Work closely with business stakeholders to understand their data requirements and translate them into effective architectural solutions.
• Foster a data-centric culture across the organization, promoting awareness and understanding of data governance principles.