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