About the Team
We’re rethinking the way AB InBev does business with its retail customers and creating digital experiences to serve them. You will be joining a new digital organization within AB InBev consisting of digital strategy, product, design, analytics, operations and engineering. This organization is responsible for building the products and platforms that transform our traditional sales operations across the world.
About the Job
Data engineer will be in charge of high-quality, scalable and resilient distributed systems that power data exploration, model building and production models.
- Our core systems need to work seamlessly across different execution contexts (real-time, near real-time and batch).
- Support diverse data analytics stacks such as Spark, Hadoop, Kafka, Cassandra and beyond.
- Work at an unusual intersection of huge data volumes and adversaries that are continuously adapting, which means we are operating at and beyond the limits of conventional alternative data systems.
- On our team you can be sure that every commit you make will come with the satisfaction that you are helping protect and improve the user experience of hundreds of millions of users.
- This role requires in-depth knowledge with cutting-edge data analytics technologies.
- Tuning, troubleshooting and scaling these big data technologies are a key part of our work, where having a curiosity with the internal workings of these systems is key to being successful.
- This is a hard-core software engineering role, where a large part of an engineer's time is spent writing code with the remainder being spent on designing and architecting systems, tuning and debugging alternative data systems, supporting production systems and supporting our data scientists.
- MS or BS in Computer Science or related field
- 6 or more years of experience building large-scale distributed systems
- Design and execution of data architectures, such as lambda and streaming
- Proficient in Python, Java or a similar language
- Message queues, notebooks, scheduling concepts and serialization formats
- Data lakes in cloud – preferable Microsoft Azure
- Data storages and databases;
- Hadoop ecosystem.
- Exceptional analytical and programming skills
- Superior knowledge with at least two of the following: Spark, MapReduce, HDFS, Cassandra, Kafka