Wellhub
Senior Analytics Engineer| Partners
Vaga remota de Analytics Engineering com fit claro de localização do candidato.
Publicada15 de jul. de 2026
Países elegíveis66 países aceitos
Sinal de senioridadeSenior
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Resumo da vaga
Senior Analytics Engineer| Partners
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
YOUR IMPACT
- Design, build, and maintain high-quality data pipelines and data models from scratch within a data mesh architecture, ensuring reliability, scalability, and clear ownership.
- Evolve existing data assets and processes, raising the bar on data standards, definitions, and governance within the Partners business area.
- Build AI-powered capabilities — data/AI agents, reusable skills, and natural-language interfaces — that amplify the team's productivity.
- Model and enrich our BI semantic layer (Looker/LookML preferred) so both humans and AI tools can reliably consume it.
- Partner with Data Analysts, Data Engineers, and business stakeholders to turn loosely defined needs into clear, well-scoped technical work
- Communicate complex technical concepts and trade-offs clearly to non-technical audiences.
- Live the mission: inspire and empower others by genuinely caring for your own wellbeing and your colleagues. Bring wellbeing to the forefront of work, and create a supportive environment where everyone feels comfortable taking care of themselves, taking time off, and finding work-life wellness.
WHO YOU ARE
- 3-5+ years in analytics engineering, data engineering, or closely related fields, with strong hands-on experience in complex pipelines, data modeling, and data architecture.
- Excellent SQL and production-grade Python (automation pipelines, not just exploratory code).
- Strong command of the full data lifecycle; experience in a data mesh (or similarly decentralized) architecture is a strong plus.
- Hands-on experience with a semantic layer or BI modeling tool (LookML/Looker strongly preferred).
- Practical knowledge of AI/LLMs: how LLMs consume structured context, and experience designing context for AI agents (writing instructions, scoping boundaries, building agent capabilities).
- Strong autonomy and organization — thrives in unstructured, high-demand environments and prioritizes ruthlessly.
- Strong execution and ownership, driving work end-to-end from ambiguous problem to reliable solution.
- Advanced English written and verbal communication skills.
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