Data Services Manager

About Ashley
Ashley is the largest furniture manufacturer and retailer in North America, and we're redefining how customers shop for furniture. We're building intelligent, conversational agents that go beyond answering questions—they initiate value-driven conversations, design rooms, and orchestrate complex purchase flows across a two-sided marketplace connecting customers with merchants and retailers.

About the role:
The Data Services Manager will be responsible for managing database systems, data warehousing solutions, and ensuring the integrity and security of our data assets. This role requires proficiency in both SQL and NoSQL databases, as well as familiarity with ETL tools, machine learning concepts, AI governance, and various programming languages.

Key Responsibilities

Database Management

• Design, implement, and maintain SQL and NoSQL databases.
• Optimize database performance and ensure data integrity and security.
• Ensure data infrastructure supports AI and machine learning workloads.

ETL Processes

• Design and manage ETL processes to extract, transform, and load data from various sources.
• Collaborate with stakeholders to identify data requirements and ensure data quality.
• Implement data pipelines that support AI/ML workflows and real-time data processing needs.

AI Governance and Oversight

• Establish and maintain AI governance frameworks to ensure responsible AI development and deployment.
• Implement policies for ethical AI use, including bias detection, fairness evaluation, and transparency standards.
• Collaborate with legal and compliance teams to address AI-related risks and regulatory requirements.
• Ensure human oversight mechanisms are in place for AI-generated outputs and decisions.

Machine Learning Integration

• Support machine learning projects by preparing and managing data for analysis.
• Collaborate with data scientists to implement machine learning algorithms and models.
• Manage data quality and validation processes to ensure AI model accuracy and reliability.
• Oversee data labeling, annotation, and versioning for machine learning datasets.

Generative AI Data Management

• Establish data management protocols for generative AI applications and large language models.
• Implement safeguards to prevent data leakage and ensure secure AI model training.
• Monitor and validate outputs from generative AI systems to maintain quality standards.
• Develop data lineage and provenance tracking for AI-generated content.

Data APIs

• Develop and maintain APIs for data access and integration with other systems.
• Ensure APIs are secure, efficient, and well-documented.
• Create API interfaces that support AI model serving and inference endpoints.
• Develop automation scripts for AI model monitoring and data quality validation.

Distributed Systems

• Understand and apply the basics of distributed systems in data management.
• Collaborate with infrastructure teams to ensure data systems are scalable and resilient.
• Design distributed data architectures that support large-scale AI processing requirements.

Qualifications

• Bachelor’s degree in Computer Science, Information Technology, or a related field.
• Proven experience in database management, including SQL and NoSQL technologies.
• Familiarity with data warehousing concepts and ETL tools.
• Familiarity with data privacy regulations and best practices (GDPR, CCPA, AI-specific regulations).
• Experience in machine learning frameworks and data analysis.
• Understanding of AI governance principles and responsible AI practices.
• Knowledge of generative AI technologies and their data requirements.
• Proficiency in programming languages: Python, Java, and Scala.
• Understanding of distributed systems and their implications for data management.
• Strong problem-solving skills and ability to work collaboratively in a team environment.
• Awareness of AI ethics, bias mitigation, and fairness in AI systems.

Preferred Skills

• Experience with cloud-based data solutions (e.g., AWS, Azure, Google Cloud).
• Knowledge of data visualization tools and techniques.
• Experience implementing AI governance frameworks and policies.
• Understanding MLOps practices and AI model lifecycle management.
• Familiarity with vector databases and embeddings for AI applications.
• Experience with AI model monitoring, explainability, and interpretability tools.
• Knowledge of federated learning and privacy-preserving AI techniques.