Senior Data Analytics

About the role

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

1. Analytics Engineering — the core of the role

• Own and evolve the semantic layer: curated, documented, and tested dbt models that serve BI, self-service analytics, and ML feature needs
• Define and maintain KPI definitions across business domains (Sales, Marketing, Finance, Supply Chain, eCom) — the single source of truth the whole organization relies on
• Drive data quality, documentation, and observability practices — a broken data contract is treated like a bug in production
• Collaborate with Data Engineers on pipeline design and data availability, and with the Data Scientist on feature engineering and model readiness
• Contribute to the semantic layer evolution roadmap as part of the SPINE program

2. 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

3. 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

Experience

• 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