Business Analyst (Reach & On Premise)

This role organises and structures On Premise data assets so the channel can be reported, analysed, automated and prepared for AI. It owns the market read behind REACH, Red Bull's programme to drive horizontal distribution, and provides hands-on analytical and reporting support across the Commercial business. The role works closely with the REACH Strategy Manager, Data Science at HQ, Performance Insights, and On Premise sales, field and channel teams.

• Build and own the total GB outlet universe, defining what counts as an outlet in each channel and sizing and segmenting it using licensed premises data, food business registers, points of interest data, wholesaler and distributor records, and field sales data.
• Work with Data Science at HQ to design methodologies for estimating the universe and distribution in channels where no single provider covers the market, including sampling approaches, extrapolation, weighting and stated confidence levels.
• Establish and run a monthly tracking cycle for horizontal distribution with the REACH Strategy Manager, covering numeric distribution by channel and account type and rules on versioning, restatement and re-basing.
• Validate modelled estimates against ground truth from field audits, wholesaler and distributor sell-out data and customer data, documenting known gaps, biases and margins of error.
• Turn the market read into action by producing prioritised outlet target lists, white space maps and channel opportunity sizing for field sales and channel teams.
• Translate business questions into data requirements, act as a liaison between business users and technical teams, and work with the Performance Insights team and On Premise sales, field and channel teams to define KPIs and keep metrics and reporting consistent.
• Proactively challenge requests and assumptions where a different analytical approach would better answer the business question, and establish agreement where teams have different definitions, requirements or interpretations of performance.
• Analyse large volumes of structured and unstructured internal and external market data, using SQL, Power BI and Excel to independently interrogate, combine and analyse complex commercial datasets.
• Design and document quantitative methods for incomplete data, including sampling, estimation, extrapolation and sensitivity analysis
• Develop and maintain robust approaches to entity matching and de-duplication
• And use SQL and other programming tools such as Python to clean, transform, reconcile and prepare messy outlet names, addresses and geospatial data.
• Identify and resolve data quality issues, leverage Snowflake, Power BI or other BI platforms to develop and maintain On Premise reporting and data visualisation, automate recurring reporting and manual data preparation
• Prepare On Premise data assets for AI-enabled use cases through clear definitions, documented lineage and structured context, and stay informed about emerging technologies and methods in automation, data analytics and FMCG business processes.

• Curiosity: continuously eager to learn, improve systems, processes and tools, and uncover unexpected connections between data points that lead to valuable insights.
• Quantitative methods: comfortable with sampling, estimation and extrapolation, and able to explain the confidence and limitations of a modelled number to a non-technical audience.
• Strong SQL skills, with the ability to independently interrogate and analyse complex datasets.
• Strong Power BI and Excel skills, covering reporting, modelling and ad-hoc commercial analysis, with understanding of how to tailor reported outputs for a range of audiences.
• Experience building a usable view of a market from fragmented or imperfect sources, including entity matching and de-duplication, rather than only working with clean measured data.
• Data literacy: ability to prepare high-quality datasets that preserve the core meaning of the data, enabling quick understanding of its relevance and implications for the problem at hand.
• Communication: a great communicator at all levels, capable of managing stakeholders' expectations and defending a methodology under challenge.

• Experience applying Python or R to analysis or automation challenges is a strong plus.
• Snowflake experience is a plus.
• Prior experience working at an FMCG company in a data or analytics focused role is a plus.
• Experience of On Premise, foodservice or wholesale channels is a strong plus.