Data Platform Engineer

As a Data Platform Engineer at AppLovin, you'll be entrusted with designing, developing, and maintaining our large-scale distributed systems. You'll work closely with various engineering teams to meet diverse technological challenges, ensuring the seamless processing of over 6PB of data and providing reliable systems that cater to 1.4 billion users daily.

To qualify for the Data Platform Engineer position at AppLovin, you should possess at least a Bachelor's or Master's Degree in Computer Science or a related field and a minimum of one year of professional experience using Java or Scala. Familiarity with big data systems like Apache Spark and a strong understanding of algorithms will set you apart!

In your role as a Data Platform Engineer at AppLovin, you will work with an impressive tech stack, including Java, Scala, Spark, Airflow, and GCP, among others. Additionally, you'll be dealing with a variety of databases and cloud services as you tackle the challenges presented by big data processing and distributed computing.

At AppLovin, collaboration is a core value, especially on the Data Platform team. You'll have the chance to work closely with engineers from various teams, sharing insights and tackling different technical challenges together. We believe in building an inclusive culture that empowers every team member to contribute their unique perspectives and skills.

AppLovin is recognized as one of the Best Workplaces in the Bay Area, and our commitment to creating a supportive and diverse work environment makes us an ideal place for a Data Platform Engineer. With a focus on personal growth, competitive compensation, and a vibrant team culture, you’ll find fulfilling opportunities to excel and innovate here.

When answering this question, detail specific projects or tasks where you've used Java or Scala and how these experiences have honed your skills for handling data effectively, especially in large-scale environments.

Describe a systematic approach where you identify, diagnose, and resolve issues within complex big data systems, possibly illustrating a past scenario that showcases your problem-solving skills.

Be prepared to discuss any practical projects where you've utilized Apache Spark for data processing and the impact of its use on the overall performance of systems you've developed or maintained.

Discuss your experience deploying solutions in GCP or AWS environments, focusing on the scalability and reliability you have achieved through these platforms.

Highlight strategies or methodologies you employ to maintain data integrity, such as validation techniques, redundancy checks, or any experience with data governance.

Emphasize the importance of collaboration in engineering workflows and provide examples of how you've effectively worked within teams to reach a common goal, showcasing your ability to communicate and share knowledge.

Illustrate your commitment to continuous learning through resources like online courses, tech blogs, or participation in forums and how this helps you incorporate best practices into your work as a Data Platform Engineer.

Be specific about a challenging situation regarding data handling you encountered and the steps you took to devise a solution, ensuring to convey the significance of the problem and impact of your resolution.

Detail the thought process you follow when designing a system for scalability, including considerations such as architecture, data flow, and handling increased loads strategically.

Discuss various metrics you monitor, such as processing time, data accuracy, and system throughput, explaining how these metrics help you refine performance and guarantee efficient operations.