Embedded Analytics (light, bounded)

• Customize and update Embeddable component files pulled into the client's repo
• Maintain theming and keep the Embeddable SDK current
• Note: Embeddable itself handles the builder, embed serving/rendering, security tokens, and multi-tenancy: this is a maintenance layer, not a build-from-scratch effort

Delivery & Documentation

• Maintain centralized documentation in Confluence, including DBML/database diagrams
• Capture ongoing knowledge for handover and continuity purposes
• Work to defined outcome targets from month two: P1 issue resolution/mitigation within one business day, defect reduction against an agreed baseline, and business-hours availability once the client is live
• Use spare capacity (when live issues don't consume the monthly band) on preventative maintenance, hardening, and onboarding new data sources/integrations

Required Skills & Experience

• AI-first delivery (core requirement): hands-on with Claude Code (or similar) / AI-assisted engineering across the full development lifecycle; Anthropic's professional courses and certification are a strong plus (or readiness to complete them)
• Full-stack development experience, including a modern front-end framework (e.g. React / TypeScript), authentication/SSO implementation, and multi-tenant application architecture
• Hands-on experience with Dagster for orchestration (or similar tools)
• Strong DBT and SQL experience for data transformation
• Experience with BigQuery and the Google Cloud Platform (GCP) data stack
• Experience with Cube or a comparable semantic-layer / metrics-layer tool
• Familiarity with embedded analytics tooling (Embeddable or similar), component customization, theming, SDK integration
• Comfortable working independently and engaging directly with client stakeholders
• Strong documentation discipline: Confluence, DBML/ER diagrams
• Available to work core hours aligned to Chicago time (CT)

Nice to Have

• Background supporting analytics/BI platforms for enterprise or sports/media clients
• Experience setting SLA style targets (resolution time, availability) and reporting against them

Engagement Details

• 80 hours/month, full remit across platform shell, data/semantic stack, and Embeddable upkeep
• Backup coverage required for continuity during absences: candidate should be able to hand off context cleanly