Lead data modeling and engineering across advanced data platforms
Architect and deliver scalable cloud data solutions leveraging platforms such as Snowflake
Lead the design and implementation of cloud‑native architectures
Oversee development and execution of test plans/scripts and thorough data validation
Drive root cause analysis for production data/AI issues
Collaborate with backend engineering and other technical teams across Digital
Provide guidance and may lead/co-lead moderately complex projects
Stay current on AI/ML advancements and apply them to enterprise initiatives

Bachelor’s degree (Master’s preferred) in Computer Science, Data/AI/ML, Engineering, or related field
4+ years in data engineering/architecture (or equivalent)
2+ years delivering GenAI & AI/ML solutions in production (preferred)
Strong Python engineering (production coding, packaging, testing)
Strong SQL
Snowflake: data modeling, performance patterns, security concepts, and operational usage
Experience designing and operating cloud‑native platforms , including microservices architectures, containerization (e.g., Kubernetes)
Cloud architecture for data platforms (e.g., Azure/AWS)
Building/operating ETL/ELT pipelines with CI/CD automation, monitoring, and data quality controls
Demonstrated experience implementing enterprise data quality standards
Experience delivering GenAI & AI/ML solutions end-to-end
Familiarity with LLM concepts and practices
Hands‑on experience with vector databases and embedding‑based retrieval patterns
Experience working with unstructured and semi‑structured data
Proven ability to lead/co-lead moderately complex projects
Strong communication skills: can translate technical decisions and tradeoffs to non-technical stakeholders

Manager AI, Data Architect, AI Engineer

Key skills