Apple Ads is building the next generation of privacy-focused advertising capabilities and needs a Software Engineer to build the future of their real-time stream processing systems.
Requirements
- 8+ years of industry experience building scalable data pipelines and data platforms
- Strong computer science and data engineering fundamentals
- Expertise in programming languages such as Python/Java/Scala
- Expertise in NoSQL datastores (e.g. Cassandra, Keyspaces, ElastiCache)
- Expertise in relational databases and SQL
- Expertise in distributed systems and data processing technologies (e.g. Spark, Kafka, Flink)
- Experience building and scaling systems on premise and in the cloud
Responsibilities
- Engineer secure and scalable data processing systems across different execution contexts such as real-time, near real-time, and batch using Spark, Flink, Hadoop, Kafka, Cassandra and beyond.
- Collaborate with a team of world-class data infrastructure engineers and product managers to apply leading-edge techniques and technologies that process petabytes of realtime streaming events.
- Lead a group of talented data infrastructure engineers and help align the technical direction across the entire Ad Platforms data organization.
- Designing and implementing core stream-processing capabilities
- Supporting privacy preserving critical pipelines
- Define, refine, and refactor approaches, designs, and architectures to meet the data engineering challenges
- Building data products that deliver on Apple's privacy commitments and change the way advertising works with data
Other
- You will collaborate closely with the business to deliver relevant data and insight to inform our strategy and decisions.
- You should have 3+ years experience in data engineering roles, ideally within the ads or media space.
- You will have an excellent understanding of scalable approaches and thrive working in Agile environments.
- The ability to be a good standout colleague under tight deadline constraints is key to success!
- Ability to communicate effectively with multi-functional technical and non-technical teams
