Senior Data & AI Analyst

Quick Facts

• Analytical role owning the end-to-end analytical lifecycle of AI solutions
• Focus on requirements, testing logic, quality measurement, PoCs, and adoption

Description

You will be responsible for the entire analytical lifecycle of the solution, from defining business requirements and testing logic to ensuring a successful release and driving adoption across the organization. You will build and refine the mechanisms that measure the quality of the AI's responses, including defining key metrics, curating high-quality Q&A datasets for fine-tuning, and tracking performance over time to ensure the system is not just smart, but also trustworthy. You will work hand-in-hand with Data Engineers to design and implement robust data models (star schema), propose data quality checks, and support the deployment process.

You will also be a key partner for business stakeholders, translating their needs into technical requirements and presenting the power of AI at internal 'AI Office Hours.' You will lead and validate Proof-of-Concept (PoC) projects for new AI use cases, constantly evaluating and comparing alternative solutions to ensure the platform stays on the cutting edge. You will diagnose and escalate technical issues related to the AI stack, including LLMs (like Claude), BI connectors, and other integrated tools.

Benefits

• Opportunity to shape end-to-end AI analytics processes from requirements through release and adoption
• Build and refine AI response quality measurement and evaluation mechanisms over time
• Collaborate with Data Engineers and business stakeholders, including internal 'AI Office Hours'
• Lead and validate PoCs for new AI use cases and keep the platform on the cutting edge

Requirements

• 5+ years of experience in a highly analytical role, such as Business Analytics, Data Analytics, or Analytics Engineering
• Expertise bridging deep technical details and high-level business objectives
• Expert SQL skills querying and working with very large, complex datasets on a modern cloud data platform (Databricks strong advantage)
• Data warehousing best practices knowledge including dimensional data modeling (star schema) and ETL/ELT processes
• Hands-on experience with LLM-based AI tools (e.g., Claude or similar)
• Principles of building and evaluating conversational AI (quality metrics, Q&A pairs, ground truth)
• Excellent communication skills to translate business needs into technical requirements for technical and non-technical audiences
• Data governance understanding and familiarity with data cataloging tools (e.g., Atlan or similar)
• Comfort with ambiguity; independently drive complex, multi-step projects (roadmaps and PoCs) from start to finish
• Fluency in Polish and English