Data Architect

About This Role

This role is for a Data Architect to design modern, AI-ready data architectures for client engagements, focusing on data modeling, semantic layer design, feature engineering, and AI enablement. The architect will ensure data is reliable, reusable, and production-ready for business intelligence, machine learning, and artificial intelligence applications.

Responsibilities

• Design and implement end-to-end data architectures in Snowflake from raw ingestion through staging, fact/dimension modeling, and semantic layer design
• Define data models balancing 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 for 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
• Implement versioned datasets and definitions to support reliable analytics and reporting
• 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
• Monitor data quality and prevent data leakage that could affect model performance
• Support event-driven architectures that bridge predictive models with operational systems
• Partner with AI teams to integrate structured and unstructured data into generative and agentic workflows
• Design APIs or event structures that serve predictions and triggers in near real time
• Measure adoption and value of AI-driven workflows through data instrumentation

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 (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
• Ability to operate 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 (consulting/agency or internal platform teams with cross-functional stakeholders)
• Outstanding written/verbal communication (executive briefings, workshops, decision memos)
• Commitment to ethical practices and responsible AI

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) including ingestion, embeddings, retrieval, generation, and evaluation
• Working knowledge of LLM behavior (tokens, context windows, temperature/top-p, few-shot/tool use) and how to tune for quality/cost/latency
• Comfort with vector search (e.g., pgvector or a hosted vector store) and hybrid retrieval patterns
• Evaluation & safety basics: offline evaluation harnesses, lightweight online A/B tests, and guardrails for PII and prompt-injection
• MLOps for LLMs: experiment tracking, versioning of prompts/configs, CI/CD for data & retrieval graphs, and production monitoring (latency, cost, drift)
• Python scripting for data/LLM utilities and service integration (APIs, batching, retries)
• 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 (role‑based access, least privilege, data retention)

Skills

* Required skills

Benefits