Data Architect - Chicago, IL

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 (consulting/agency or internal platform teams with cross-functional stakeholders). Outstanding written/verbal communication (executive briefings, workshops, decision memos). Basic scripting ability in Python (preferred). Practical Generative AI experience (preferred). Working knowledge of LLM behavior (preferred). Comfort with vector search (preferred). Evaluation & safety basics (preferred). MLOps for LLMs (preferred). Python scripting for data/LLM utilities and service integration (preferred). Familiarity with BI tools (Power BI/Tableau) and semantic layer design (preferred). Exposure to streaming, reverse ETL, and basic MDM/reference data management (preferred). Security & governance awareness (preferred).

Design and implement end-to-end data architectures in Snowflake. Define data models balancing flexibility, performance, and scalability. Partner with engineering teams to integrate data from source systems. Establish versioned modeling standards and documentation. Build or refine semantic layers for unified metric definitions. Collaborate with business owners to define and approve KPIs. Implement versioned datasets and definitions. Architect feature pipelines and data contracts for ML models. Collaborate with data scientists/AI engineers on feature stores. Monitor data quality and prevent data leakage. Support event-driven architectures for predictive models. Integrate structured and unstructured data into generative AI workflows. Design APIs or event structures for near real-time predictions. Measure adoption and value of AI-driven workflows.