AI Senior Support Engineer
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
TL;DR
AI Senior Support Engineer (Claude Code/Analytics Platform): Own and maintain an analytics platform across the platform shell, data ingestion + semantic layer, and bounded embedded analytics upkeep with an accent on AI-first delivery using Claude Code and outcome-based support targets. Focus on solving production issues, extending ingestion/orchestration/transformations, and keeping embedded components and documentation continuously up to date.
Location: Remote (Argentina, Uruguay, Colombia)
Company
staffs a Senior Support Engineer for a client engagement supporting an analytics platform.
What you will do
- Maintain and extend authentication and SSO integration, and own the platform shell and navigation components.
- Build and maintain data ingestion pipelines, orchestration workflows in Dagster, and transformation logic in dbt and SQL.
- Operate the data stack on Google Cloud Platform (GCP), primarily BigQuery, and maintain the semantic layer in Cube (metrics and data modeling).
- Provide bounded maintenance for embedded analytics components (customization, theming, and keeping the Embeddable SDK current).
- Deliver outcome-based support from month two: P1 issue resolution/mitigation within one business day, defect reduction vs baseline, and business-hours availability once the client is live.
- Maintain centralized documentation in Confluence (including DBML/database diagrams) and ensure clean backup handoff coverage.
Requirements
- AI-first delivery: hands-on with Claude Code (or similar) across the full development lifecycle; Anthropic professional courses/certification is a strong plus.
- Full-stack experience with a modern frontend framework (e.g., React/TypeScript), authentication/SSO, and multi-tenant application architecture.
- Hands-on experience with Dagster for orchestration (or comparable tooling).
- Strong dbt and SQL experience for data transformation.
- Experience with BigQuery and the Google Cloud Platform (GCP) data stack.
- Availability aligned to Chicago time (CT) and ability to work core hours.
Culture & Benefits
- Remote engagement with 80 hours/month and a commercial-model first approach (observe/define in month one, outcome-based delivery from month two).
- AI-augmented delivery using Claude Code inside the client’s own Claude Code/AI-tooling accounts.
- Outcome targets include P1 resolution within one business day, defect reduction against baseline, and business-hours availability once live.
- Spare capacity used for preventative maintenance, hardening, and onboarding new data sources/integrations.
- Backup coverage required to ensure continuity and clean context handoff during absences.
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