Passion for Data? And for Analytics? Check this out!
The Analytics Engineer is part of the Data Science Growth team within the broader Data Science & Analytics organisation. This role focuses on delivering key strategic initiatives in Data Harmonisation and Data Provisioning. The role’s primary mission is to ensure that critical external data sources (e.g. sell-out, sell-in, store-level, eCommerce, and shopper data) are harmonised and quality-assured in line with Red Bull’s business logic.
The role is also responsible for building and scaling this capability in a cost-efficient manner as well as running it on an ongoing basis.
RESPONSIBILITIES
Areas that play to your strengths
All the responsibilities we'll trust you with:
1. You’ll lead efforts to standardise, harmonise, and assure the quality of critical external data sources, ensuring that business data is accurate, consistent, and ready for analytics, reporting, and ML across the organisation. This includes ongoing quality assurance and continuous improvement of data processes.
2. You’ll take ownership of implementing new product features and improving existing ones, always seeking opportunities to innovate and deliver greater value to the business
3. You’ll work closely with both internal and external data engineering teams, fostering strong partnerships and ensuring seamless integration of data initiatives. Your collaborative mindset will maximise the impact of cross-functional projects and support organisational goals.
4. You’ll design, build, and maintain robust data pipelines, leveraging machine learning and AI to unlock insights and automate processes. Your technical expertise will be key to delivering scalable, high-performance analytics solutions
5. You’ll manage project timelines, proactively communicate progress, and address challenges to ensure successful and timely delivery of all initiatives. Your ability to manage up and keep stakeholders informed will be essential to the role’s success.
6. Data Engineering Expertise (SQL, Python, dbt, git – Snowflake experience preferred)
7. Data Modeling & Harmonisation
8. Product Mindset (incl. drive to succeed and get adoption; pipeline management)
9. Analytics & Business Acumen (incl. understanding of Red Bull specific data and taxonomies)
10. Relevant ML & AI Familiarity (incl. heuristics, embeddings models, LLMs, …)
11. Data Governance & Quality Assurance.
12. Ability to communicate to technical and non-technical audiences.
13. FMCG Data Expertise (e.g., Nielsen/Circana data, Retailer data, …)
14. Strong communication and presentation skills.
15. Team-player and collaborative (“copy-left” vs “copyright”).
16. Proactive, self-motivated, and able to work on different projects in parallel.
17. Travel 0-10%
Analytics Engineer
Red Bull
Giving wiiings to people and ideas since 1987
In the 1980s Dietrich Mateschitz developed a formula known as the Red Bull Energy Drink. This was not only the launch of a completely new product, in fact it was the birth of a totally new product category.
What drives usChasing our potential
Since the early days of Red Bull, an entrepreneurial mindset has always guided our approach to work and the environment we create: