Business Analyst (Reach & On Premise)

REACH UNIVERSE SIZING AND TRACKING

• Build and own the total GB outlet universe: define what counts as an outlet in each channel, then size and segment it by combining licensed premises data, food business registers, points of interest data, wholesaler and distributor records, and our own field sales data.
• Work with Data Science at HQ to design methodologies for estimating the universe and our distribution in channels where no single provider covers the market, including sampling approaches, extrapolation, weighting and stated confidence levels.
• Working closely with the REACH Strategy Manager, establish and run a monthly tracking cycle for horizontal distribution: numeric distribution by channel and account type, with clear rules on versioning, restatement and when the universe is re-based.
• Validate modelled estimates against ground truth from field audits, wholesaler and distributor sell-out data and customer data, and document known gaps, biases and margins of error openly.
• Turn the market read into action: prioritised outlet target lists, white space maps and channel opportunity sizing that field sales and channel teams can work from directly.

STAKEHOLDER COLLABORATION

• Translate business questions into data requirements and ensure data assets meet these needs.
• Act as a liaison between business users and technical teams (i.e. IT, Data Science at HQ, external data providers).
• Work with the Performance Insights team and with On Premise sales, field and channel teams to define key performance indicators (KPIs) and keep metrics and reporting consistent across the channel.
• Proactively challenge requests and assumptions where a different analytical approach would better answer the business question. Establish agreement where teams have different definitions, requirements or interpretations of performance.
• Communicate complex analysis clearly to both technical and non-technical audiences.

DATA ANALYSIS & MODELLING

• Analyse large volumes of structured and unstructured data from various sources: internal and external market data.
• Use SQL, Power BI and Excel to independently interrogate, combine and analyse complex commercial datasets.
• Design and document appropriate quantitative methods for incomplete data, including sampling, estimation, extrapolation and sensitivity analysis.
• Develop and maintain robust approaches to entity matching and de-duplication.
• Use SQL and other programming tools (e.g., Python) to clean, transform, reconcile and prepare data for analysis, including messy outlet names, addresses and geospatial data, selecting the most efficient tool for the job.
• Identify and resolve data quality issues affecting analysis and reporting.

REPORTING, AUTOMATION & AI

• Leverage tools such as Snowflake, Power BI, or other BI platforms to develop and maintain On Premise reporting and data visualisation needs.
• Automate recurring reporting and manual data preparation so that the team spends its time interpreting numbers rather than assembling them.
• Prepare On Premise data assets for AI-enabled use cases through clear definitions, documented lineage and structured context, so that AI tools return answers the business can trust.
• 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, while uncovering 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 the limitations of a modelled number to a non-technical audience.

SQL: Strong SQL skills, with the ability to independently interrogate and analyse complex datasets.

BI & Excel: Strong Power BI and Excel skills, covering reporting, modelling and ad-hoc commercial analysis. Understanding of how to tailor reported outputs for a range of audiences.

Programming: Experience of applying Python or R to analysis or automation challenges is a strong plus. Snowflake experience is a plus.

Working with Incomplete Data: 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.

FMCG Knowledge: 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.

PERFORMANCE GOALS:

• Deliver a documented On Premise universe methodology covering all priority channels in the first six months. Validate against an independent field audit sample each quarter, with modelled outlet status matching audit findings within an agreed tolerance.
• In the first eight weeks, baseline the recurring reporting and data preparation effort across the On Premise team. By year end, remove an agreed number of hours per month, with each solution documented, handed over, and running for three consecutive cycles without manual intervention.
• Prepare the core On Premise datasets for AI use: clear definitions, documented lineage and appropriate data quality controls, covering the agreed priority domains. Contribute subject matter expertise, business definitions and test scenarios to AI initiatives led by the wider Data & AI team.

At Red Bull, we exist to give people wiiings. To achieve that, we recognise the value that comes from having a highly diverse workforce that has the freedom and responsibility to realise their ideas and seize opportunities. We make recruiting decisions based on experience, skills, potential and talent: everyone is welcome here.