Experienced Data Engineer - Streaming Platform

As an Experienced Data Engineer at Voodoo, you'll be tasked with building and optimizing real-time data pipelines that process an immense volume of bid requests and user engagement metrics. You'll collaborate with backend engineers to facilitate the integration of OpenRTB signals into our systems, ensuring that data flows smoothly across platforms. Additionally, you will have the opportunity to write clean, well-documented code in Java, Scala, or Python, and work with event platforms like Apache Pulsar and Kafka to guarantee reliable message delivery.

To apply for the Experienced Data Engineer role at Voodoo, candidates should have 3-5+ years of experience in data engineering, particularly with real-time streaming systems. Familiarity with stream processing tools (like Apache Flink or Spark Structured Streaming) and programming skills in Java, Scala, or Python are essential. You should also have exposure to cloud environments such as AWS or GCP and be comfortable managing workloads in Kubernetes.

The Experienced Data Engineer position at Voodoo requires a solid understanding of programming in languages such as Java, Scala, or Python. Proficiency in these languages is essential for developing data pipelines within distributed systems and managing event-driven data processing efficiently.

Voodoo is committed to supporting its Employees in the Experienced Data Engineer role through a competitive salary structure and comprehensive relocation packages for new hires. Additionally, Voodoo believes in a balanced work-life approach, offering wellness activities, unlimited vacation, and premium healthcare coverage to ensure you can thrive both personally and professionally.

As an Experienced Data Engineer at Voodoo, you'll be working with an array of advanced tools and technologies including Apache Flink, Spark Structured Streaming, GCP Pub/Sub, AWS Kinesis, and messaging platforms like Kafka. You'll also help manage event schemas using frameworks such as Avro or Protobuf, and have the chance to contribute to improving our data infrastructure.

In responding to this question, focus on specific examples of real-time data projects you've worked on. Highlight the technologies you utilized, such as Apache Flink or Spark Structured Streaming, and the outcomes you achieved. Emphasize your understanding of the challenges involved and how you ensured high throughput and low latency.

Data integrity is crucial in streaming applications. To ensure it, elaborate on techniques you've used for monitoring data flow, implementing logging and alerting measures, and how you handle failures. Discuss any tools or frameworks you've found effective in maintaining data accuracy and consistency.

Think of a specific instance where you had to overcome a significant challenge in data processing. Discuss the problem, your approach to solving it, and the technologies you employed. Be sure to convey how you optimized the process or improved performance as a result of your actions.

Collaboration with backend engineers is essential, especially when integrating systems. Discuss how you ensure clear communication regarding data requirements and flow. Mention any collaborative tools or practices that have helped you work effectively together.

Highlight your commitment to continuous learning in the data engineering field. Discuss any blogs, forums, or resources you follow, along with any professional groups or meetups you attend. Mention how keeping up with industry trends helps you innovate in your role.

Discuss your experience with cloud platforms like AWS or GCP and the specific services you have deployed. Explain how cloud solutions have enhanced your data engineering tasks, such as scalability and reliability, in your work on data pipelines.

While answering, define OpenRTB and discuss its significance in the context of programmatic advertising. Talk about your understanding of its impact on data engineering and how integrating its signals can enhance real-time bidding processes.

List the tools you have used for monitoring and logging, explaining why they are effective. Discuss how you implement these tools in your projects to ensure visibility into system performance and reliability.

Explain your familiarity with CI/CD practices and highlight any tools you've used, such as Git, Jenkins, or GitLab. Discuss how implementing CI/CD pipelines has contributed to your efficiency in deploying data processing workloads.

Schema evolution can be tricky, but it’s essential. Discuss your experience managing changes in data schemas, using tools like Protobuf or Avro. Provide an example of how you've successfully managed schema updates without disrupting data integrity.