Business Analyst (Reach & On Premise)

Company Description

Red Bull UK is currently recruiting for a Business Analyst (Reach & On Premise) to be based in London, UK.

The core purpose of this role is to organise and structure our On Premise data assets so that the channel can be reported, analysed, automated and prepared for AI. On Premise data is far more fragmented than the Off Premise: there is no single measurement provider covering the full outlet universe, and coverage is thinnest in exactly the channels we are trying to grow into. This role builds the data foundations that close that gap, creating structured, scalable datasets and automated reporting that make On Premise decision-making faster and better evidenced.

This role also owns the market read behind REACH, our programme to drive horizontal distribution and put Red Bull in outlets where we have never been listed. Sizing the outlet universe channel by channel, establishing where we are and are not present, and tracking horizontal distribution monthly with a methodology that stands up to scrutiny. This groundwork will lead to collaboration with the Reach Strategy Manager, translating commercial questions into structured analytical requirements and taking ownership of delivering a clear and actionable answer.

Alongside the REACH requirements, the role will provide hand-on analytical and reporting support across the Commercial business, investigating performance to answer business questions and continually improve the quality and efficiency of commercial reporting.

This role has a large part to play in both driving strategic business decisions in the long term and tracking performance short term. It has high exposure to multiple departments both locally and at our HQ in Austria, so developing excellent relationships with our internal stakeholders is key.

Every application is reviewed by a member of our recruitment team. We take a people-first approach to hiring, ensuring that every application receives thoughtful consideration

Job Description

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.

Qualifications

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.

Additional Information

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.