Data Architect

About This Role

This role will design modern, AI-ready data architectures for clients, focusing on data modeling, semantic layer design, feature engineering, and AI enablement. The architect will create systems to make data reliable and production-ready for business intelligence, machine learning, and artificial intelligence initiatives.

Responsibilities

• Design and implement end-to-end data architectures in Snowflake, from ingestion through staging, fact/dimension modeling, and semantic layer design
• Define data models that balance flexibility for analysts with performance and scalability for production
• Partner with engineering teams to integrate data from source applications and operational systems
• Establish versioned modeling standards and documentation to ensure consistency across domains
• Build or refine semantic layers that unify metric definitions across BI tools like Tableau, Power BI, or Looker
• Collaborate with business owners to define KPIs, approve new metrics, and monitor adoption
• Architect feature pipelines and data contracts that support point-in-time correctness for machine learning models
• Collaborate with data scientists and AI engineers to implement reusable feature stores for both training and deployment use
• Partner with AI teams to integrate structured and unstructured data into generative and agentic workflows

Requirements

• 7+ years in data engineering/analytics engineering with ownership of production pipelines and BI at scale
• Demonstrated success owning and stabilizing production data platforms and critical pipelines
• Strong grasp of modern data platforms (e.g., Snowflake), orchestration (Airflow), and transformation frameworks (dbt or equivalent)
• Competence with data integration (ELT/ETL), APIs, cloud storage, and SQL performance tuning
• Practical data reliability experience: observability, lineage, testing, and change management
• Operates effectively in ambiguous, partially documented environments; creates order quickly through documentation and standards
• Prior ownership of core operations and reliability for business-critical pipelines with defined SLOs and incident response
• Demonstrated client-facing experience and outstanding written/verbal communication

Qualifications

• Bachelor's degree or equivalent experience
• 7+ years in data engineering/analytics engineering with ownership of production pipelines and BI at scale

Nice to Have

• Deep interest in Generative AI and Machine Learning
• Basic scripting ability in Python
• Practical Generative AI experience: shipped at least one end-to-end workflow (e.g., RAG)
• Working knowledge of LLM behavior
• Comfort with vector search and hybrid retrieval patterns
• Evaluation & safety basics for AI
• MLOps for LLMs
• Familiarity with BI tools (Power BI/Tableau) and semantic layer design
• Exposure to streaming, reverse ETL, and basic MDM/reference data management
• Security & governance awareness

Skills

* Required skills