Senior Analytics & AI Engineer
Role details
Job location
Tech stack
Job description
We are looking for a Senior Analytics & AI Engineer who genuinely lives at the intersection of Analytics Engineering and AI - someone who understands that the quality of data models is what makes or breaks an AI product, and who knows how to build both. This is a hands-on technical role with a strong Analytics Engineering foundation and a growing AI dimension. What you will do
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Analytics Engineering - the core of the role
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Own and evolve the semantic layer: curated, documented, and tested dbt models that serve BI, self-service analytics, and ML feature needs
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Define and maintain KPI definitions across business domains (Sales, Marketing, Finance, Supply Chain, eCom) - the single source of truth the whole organization relies on
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Drive data quality, documentation, and observability practices - a broken data contract is treated like a bug in production
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Collaborate with Data Engineers on pipeline design and data availability, and with the Data Scientist on feature engineering and model readiness
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Contribute to the semantic layer evolution roadmap as part of the SPINE program
- AI & Agentic - where we are heading
- Contribute to the Agentic AI POC on eCom and Marketing insights ("ChatGPT for Data") - help design what data needs to look like for an agent to reason on it* Support the Profit Margin Agent use case: from data preparation and structuring, to integration
- Help establish MLOps practices on Azure ML: model lifecycle management, monitoring, deployment standards - so the Data Scientist can ship with confidence
- Evaluate AI tooling pragmatically - bring a critical, grounded view on what fits our stack and our maturity level
- Document AI patterns and architectural decisions as we discover them, building shared knowledge for the team
- Technical Vision & Team Contribution
- Bring informed technical opinions: propose architectural decisions, evaluate tools, challenge choices with well-reasoned arguments - while staying pragmatic
- Keep up with the field (models, frameworks, patterns) and bring back what is genuinely relevant to our context - signal, not hype
- Mentor Analytics Engineers: share best practices, run code reviews, raise the bar on modeling standards
- Contribute actively to PI Planning, sprint reviews, and architecture discussions - not just executing tickets, but shaping what we build
Requirements
- 5+ years in Analytics Engineering, Data Engineering, or a similar role with strong data modeling responsibility
- Proven track record delivering production-grade dbt models and semantic layers in a complex data environment
- Hands-on experience with AI or ML tooling in a data context - not necessarily deep ML expertise, but genuine curiosity and practical engagement
- Experience working in cross-functional environments, collaborating with both technical and business stakeholders