Big Data Developer (Hybrid) - job 1 of 2

As a Big Data Developer at Nexxen, you will take charge of end-to-end development, focusing on building and designing next-gen identity solutions. Your role includes implementing high-scale Big Data solutions, optimizing data pipelines, and collaborating with various teams to ensure seamless integrations with external APIs.

Applicants interested in the Big Data Developer role at Nexxen should ideally have a Bachelor's degree in computer science or a related field, along with at least 4 years of software development experience. Proficiency in Scala, GO, Python, Java, or C# and knowledge of high-scale data systems are essential.

A Big Data Developer at Nexxen will work primarily with Scala, GO, Python, Java, or C#. Additionally, familiarity with SQL and NoSQL databases, as well as technologies like Hadoop, Spark, and Kafka, is highly advantageous.

At Nexxen, we seek a Big Data Developer who is not only a strong problem solver but also a great team player with strong communication skills. The ideal candidate should demonstrate a curious mindset, embrace collaboration, and approach their work with enthusiasm and a willingness to innovate.

The work environment at Nexxen is dynamic and supportive, focusing on collaboration without ego. As a Big Data Developer, you'll have the opportunity to work in a diverse team that values different perspectives, encouraging you to grow your skills in an innovative space.

When answering this question, highlight specific projects where you've utilized technologies like Hadoop or Spark. Discuss the challenges you faced, how you overcame them, and the impact your work had on the data processing efficiency.

Discuss methods such as using efficient data formats, leveraging partitioning strategies, and monitoring for performance bottlenecks. Provide examples demonstrating how these strategies improved the overall performance of your data systems.

Share experiences of past integrations, focusing on how you assessed API documentation, handled authentication, and ensured data quality. Mention the tools or methodologies you used for successful integration.

Be honest about your proficiency in scripting languages like Python or Scala. Share examples of how you've used these languages in real projects, emphasizing the specific tasks you accomplished.

Explain your method of prioritization, such as using task management tools, setting deadlines, or breaking down larger projects into manageable milestones. Provide an example of how this strategy improved your workflow.

Share a specific instance where you encountered a difficult challenge. Explain how you approached the problem, the solution you implemented, and the lessons learned from the experience.

Discuss how understanding user needs informs the design and implementation of data solutions. Provide examples of how customer feedback influenced your previous projects and improved data utility.

Mention specific resources like online courses, webinars, or industry conferences you follow to stay current. Highlight any personal projects or active participation in professional communities that demonstrate your commitment to continuous learning.

Detail your processes for validating and cleaning data, including the technologies and tools you use. Provide an example of how these practices ensured data integrity in a past project.

Emphasize the importance of teamwork in addressing complex problems and delivering successful outcomes. Share a past experience where collaboration led to enhanced results and how you contributed to the team dynamic.