Job Title: Data Architect
Contract or Perm: Contract to hire
Compensation: $175,000 - $200,000 Conversion salary
Remote/Hybrid: Onsite
Location: Kansas City
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.
Data Security and Privacy
Develop security protocols, such as encryption, access controls (IAM), and masking techniques to safeguard data in transit and at rest.
Data Modeling and Management
Define and standardize data architecture components, including storage solutions (data lakes, warehouses, etc.), data pipelines, and integration patterns.
Data Classification
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).
Team Collaboration and Leadership
Provide technical guidance and mentorship to data engineers, analysts, developers, and other IT teams on best practices for data management and security.
