Product Analyst
Purpose of the Role
To focus on delivering a prioritised list of product improvements and investigating new improvement and product ideas, translating identified needs into practical, deta-led enhancements. Working closely with Product Managers and Data Analysts, this role is responsible for analysing requirements, prototyping solutions and converting data assets, models and pipelines into incremental, scalable product features.
The Product Analyst bridges day-to-day product priorities and technical feasibility, ensuring improvements are designed with platform capabilities, data quality, performance and scalability in mind, and can be delivered efficiently into live products.
Product Improvement & Delivery
- Own and maintain a prioritised backlog of data-led product improvements, aligned to product strategy, customer needs and commercial value.
- Translate product requirements, customer feedback and market insights into clear analytical briefs and technical requirements.
- Break down complex product opportunities into incremental, deliverable features that can be released and iterated over time.
- Support Product Managers with impact assessment, sizing and prioritisation of enhancement opportunities.
Data Analysis & Insight
- Analyse large, complex datasets within Databricks to identify opportunities for product improvement, new data source evaluations or new features.
- Proactively evaluate existing attributes, models and outputs to identify improvements in accuracy, coverage, performance or usability.
- Design and run exploratory analysis to test hypotheses and inform product decisions.
- Define success metrics and measure the impact of released product changes.
Platform & Data Enablement
- Work closely with Data Engineering to convert analytical prototypes into production ready pipelines, features and outputs.
- Ensure product enhancements are designed with data quality, lineage, monitoring and performance in mind.
- Support the definition and documentation of data assets, features and product logic for internal and external use.
- Contribute to improving reuse of shared data assets and reducing duplication across products.
Stakeholder Collaboration
- Work collaboratively with Product, Engineering, Insight and Commercial teams to ensure shared understanding of priorities and constraints.
- Clearly communicate analytical findings, recommendations and trade-offs to nontechnical stakeholders.
- Support product rollout activity by providing insight, documentation and explanation of new features and changes.
Knowledge & Technical Skills
- Strong understanding of modern data platforms, particularly Databricks
- Practical knowledge of:
- Delta Lake architecture and versioned datasets
- Databricks notebooks (SQL and Python)
- Data pipelines, orchestration and scheduling concepts
- Solid SQL skills with the ability to interrogate datasets and validate analytical outputs
- Experience designing data products using large-scale transactional, behavioural or marketing datasets
- Understanding of data modelling concepts (fact/dimension models, feature engineering, aggregations)