AI Senior Support Engineer Hours: Aligned to Chicago time (CT) · Engagement: 80 hrs/month, full-stack + data stack remit About the Role Altoros is staffing a Senior Support Engineer for a client engagement supporting an analytics platform. This is a full-stack and data-stack role: the engineer owns the platform shell, the ingestion and semantic layer, and a bounded amount of embedded analytics component upkeep. The role is commercial-model first: month one is an observe and define phase (baseline setting, onboarding), with the engagement shifting toward outcome-based delivery from month two (issue resolution time, defect reduction against baseline, availability targets). AI-augmented delivery is central to this role and one of its most important elements. Working AI-first with Claude Code is how a single engineer credibly covers this full remit. Altoros builds its delivery on Anthropic's professional courses and certification, and the engineer uses Claude Code across the full range of work — maintenance, bug-fixing, and data work, not just new development — operating inside the client's own Claude Code / AI-tooling accounts. Scope & Responsibilities Platform Shell - Maintain and extend authentication and SSO integration - Own navigation and application shell components (modern front-end framework, e.g., React / TypeScript) - Manage tenant and user management, including multi-tenancy considerations Data & Semantic Stack - Build and maintain data ingestion pipelines - Operate and extend orchestration workflows in Dagster - Develop and maintain transformation logic in dbt and SQL - Work with the data warehouse on Google Cloud Platform (GCP), primarily BigQuery - Maintain the semantic layer in Cube, including metric definitions and data modeling 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