Nice to Have
• Working knowledge of Ruby on Rails and React
• Experience with a semantic or metrics layer (e.g., dbt Semantic Layer, headless BI)
• Understanding of CI/CD, Git-based workflows, and infrastructure-as-code
The Stack Context
• Modern data stack: Fivetran, Airbyte, dbt, Snowflake, GitHub, Terraform, or similar tools
• Application context (nice to have): Ruby on Rails, React, or similar backend/frontend frameworks
• Data modeling: SQL, analytics models, documentation, testing, naming standards, and version control
• Infrastructure: cloud-based data infrastructure, infrastructure-as-code, CI/CD, monitoring, and cloud storage
• Data workflows: ingestion, transformation, orchestration, reporting, deployment, and change management
• Reporting focus: trusted metrics, scalable reporting models, dashboards, exports, and data quality
• AI surface: data pipelines and quality practices supporting AI/LLM and agentic AI use cases (RAG, embeddings, vector stores, AI agents) alongside traditional BI