Hybrid Data Analytics Engineer

The mission of Global Analytics is to lead HEINEKEN into becoming a data-driven company and the best-connected brewer. As a team, we radiate a data-driven entrepreneurial culture to the rest of the company. We act as an incubator for smart data products in all business areas, from sales to logistics and marketing to purchasing. This approach has allowed us to grow rapidly and launch many value-creating use cases from the management of spare parts to the allocation of media spending. Our focus for the upcoming year is to scale these, and other, use cases to as many countries as possible globally.

Our team comprises data scientists, engineers, Business Intelligence Specialists, and Analytics Translators. Data Scientists build models and consult the business during the process, our Data Engineers build data platforms and ensure we have access to data, BI Specialists enable interaction and insights in our data products, and the Analytics Translators help businesses ensure the solution is fit for purpose by bridging the gap between the teams. Our global perspective has led the team to have a presence across three continents and five countries, with satellite teams in South Africa, Poland, India and Singapore. We truly are a “Global” analytics team.

We want to be collaborative, innovative, and reliable. We see collaboration as key in our global community with many different cultures, roles, and challenges. And strive to create an environment where you enjoy both the problems you are solving and the people you are solving them with. We innovate to transform HEINEKEN from a traditional to a data-driven company. We position ourselves as a business partner driving value-creating decisions and building trust in our solutions.

If these challenges sound interesting and exciting, we hope you apply. These are ambitious goals, and therefore we need your help.

Data Analytics Engineer:

• At least 2+ years of hands-on experience in data engineering or as data analyst
• Experience in developing analytical data models. Experience in building ETL data pipelines.
• Experience and willingness to perform data analysis for ETL development.
• Proficient in writing complex SQL for distributed data lake systems (Spark SQL).
• Experience with software development and practicing unit testing, git, Azure CI/CD pipelines.
• Experience in developing analytical models with DBT and/or knowledge of SAP data models is a big plus.
• Knowledge of data warehouse models. Value-driven and pragmatic mind-set: focus on getting a result in an agile manner.
• Experience and skills for configuring and developing solutions with following Azure technologies: Databricks, Data Factory, DevOps, data lake storage.

Analytics Engineer:

The AE fits well by building a Data Warehouse and Data Marts optimised for reading (reporting purposes). So, let’s cover some technical knowledge the AE should have:

Data Modelling — Since this role is a specific superpower, a person should know all modelling approaches, the pros and cons, how to make the most optimal solution for a specific company.

SQL — as with Data Engineer, it’s a crucial skill to master for any Data Savyy person, but for this role specifically. It’s your bread and butter. Not only this, but you have to know how to tweak the query to make it run blazing fast on any DB (or at least understand the query plan and optimise using it)

Data Quality/Observability/Testing — this is an interesting part. Some of the tools have this integrated, and some of it you can add additionally. But AE should have a sharp eye and add checks where trouble might occur, think about anomalies, or how to be sure that your queries are correct.

Why this is interesting, you might ask — here, you might need some expertise in testing to build your data tests isolated and separately to ensure that expected values stay the same. At least dbt tests check only after the potential damage is done, and you have to roll back some tables and make changes. Having knowledge or know-how of testing would be insanely beneficial.

Optional: some scripting knowledge to extend or add missing functionality in the Transformation Tool. This is optional because the community is huge, and other folks might have the same problems and are more experienced in contributing to the Open Source project.