About the Role 

As a Platform Data Analyst, you will own analytical workstreams end-to-end — from scoping and data modelling through to communicating findings and driving stakeholder action. You will work with large, complex datasets spanning customer orders, supplier transactions, asset utilisation, and operational data, contributing to KPI frameworks, experiment design, and AI/ML use case development — using AI tools where they genuinely accelerate work, not as a default. This is an individual contributor role: your impact comes from analytical craft, clarity of thinking, and your ability to influence decisions through insight rather than through line management or product ownership. You will sit close to platform product and engineering teams, but your remit is analytics. 

Key Responsibilities 

Analytics & Insight 

• Identify and quantify opportunities in platform data; translate findings into clear recommendations for product and business teams. 

• Design and maintain decision-oriented KPI frameworks accessible to front-line teams and senior management alike. 

• Conduct deep-dive analyses on product adoption, user behaviour, performance trends, and business impact — and design experiments (A/B testing, causal inference, significance testing) to test hypotheses that emerge. 

• Support benefit estimation and validation for platform product initiatives. 

Data Craft & Self-Sufficiency 

• Write efficient SQL and Python to extract, transform, and model data from multiple source systems. 

• Build reusable analytical datasets that support recurring analysis and reporting — working independently or alongside data engineering colleagues as needed. 

• Work within our Azure/Databricks cloud infrastructure, treating analytical datasets as data products: clear ownership, documented SLAs, versioning, and a feedback loop with end users. 

• Proactively identify data quality issues and work with upstream teams to resolve them. 

Stakeholder Engagement & Delivery 

• Partner with product owners, Business users, and engineering teams to translate analytical needs into well-defined workstreams and communicate findings through data storytelling that connects insight to business priorities and drives action. 

• Own your analytical backlog — prioritise, set realistic expectations, and manage delivery independently. 

• Flag potential bias, fairness issues, or unintended consequences in analytical outputs and model inputs before they reach decisions. 

AI/ML & GenAI 

• Build and maintain feature engineering pipelines and evaluation frameworks that enable AI/ML model development. 

• Use tools like Copilot (code completion) and Claude or ChatGPT (summarisation, documentation, data exploration) to accelerate analytical tasks — anchored to specific, validated workflows. 

• Collaborate with data scientists and ML engineers to ensure analytical outputs meet model input requirements (schema, distribution, data quality). 

Skills & Experience 

Education 

Degree in Mathematics, Data Science, Computer Science, Engineering, Natural Sciences, Economics, or a related quantitative field. 

Experience 

• 3–6 years in business analytics or data analysis, ideally in a complex, large-scale data environment. 

• Proven track record delivering end-to-end analytical projects — from scoping through to stakeholder action. 

• Experience working in or alongside product or platform teams. 

• Hands-on use of AI tools (Copilot, LLM-based analysis, prompt engineering) as part of analytical or engineering workflows is a strong advantage. 

Technical Skills 

• Strong SQL proficiency for extraction, transformation, and modelling across large datasets. 

• Python for data manipulation, analysis, and automation (pandas, numpy, and similar libraries). 

• Azure and Databricks experience; familiarity with data pipeline development, data modelling concepts, and data quality best practices. 

• Power BI or equivalent, with the ability to build clear, decision-oriented dashboards. 

• Working knowledge of AI/ML concepts and how analytical pipelines feed into model development; practical use of AI coding tools (Copilot) and LLMs (Claude, ChatGPT) for analytical tasks — with the judgement to validate outputs and know their limits. 

Behaviours & Mindset 

• Proactive and self-directed — you identify problems before being asked and take ownership. 

• Structured problem-solver — you break ambiguous questions into well-defined analytical tasks and communicate findings to diverse audiences without losing rigour. 

• Collaborative and stakeholder-savvy — you understand that great analytics only creates value when trusted and acted upon, and balance high standards with pragmatism about what fits the decision at hand. 

• AI-critical — you use GenAI tools effectively but validate outputs, flag governance concerns, and apply your own judgement before acting on recommendations.