Big Data Engineer

As a Big Data Engineer at Nexxen, you will be responsible for implementing high-scale big data solutions, contributing to our platform's infrastructure and architecture. Your tasks will include end-to-end feature development, from design to production, while collaborating with various stakeholders in Product, Engineering, and Data. You'll also research innovative technologies to build scalable data applications, ensuring that our data management platform serves the core systems effectively.

Nexxen requires candidates for the Big Data Engineer position to have over 5 years of engineering experience, with at least 4 years specifically in data engineering and java development. Proficiency in ETL processes, Hadoop, Spark, Kafka, and both SQL and NoSQL databases—including Vertica and Clickhouse—is essential. A BS degree in Computer Science or a related field is also necessary, along with experience in an Agile development environment.

To be successful as a Big Data Engineer at Nexxen, you should have a strong background in Java, with additional knowledge of Python and Scala considered advantageous. Familiarity with various technologies, such as ETL frameworks and data streaming systems, enhances your ability to effectively implement and manage big data solutions.

At Nexxen, the work environment is collaborative and dynamic. Big Data Engineers are part of a hybrid work model, allowing you to work in the office three days a week while enjoying remote work flexibility for the remaining days. Our culture emphasizes creativity, collaboration, and a strong commitment to customer-centric solutions, making it an exciting place to grow your career.

Absolutely! At Nexxen, we are committed to the professional growth and development of our employees. Big Data Engineers have the opportunity to take on significant responsibilities, engage in research and innovative projects, and develop their skills across various technologies, including AI and machine learning, while contributing to major advancements in our data solutions.

When answering this question, focus on specific projects where you utilized Hadoop, including details about data processing, scalability challenges you faced, and solutions you implemented. Highlight your understanding of distributed processing and how you've optimized Hadoop clusters to improve performance.

Discuss techniques you use to maintain data integrity, such as validation rules, quality checks at each stage of the ETL process, and tools you may have utilized for monitoring data quality. Emphasize your ability to identify and rectify data anomalies promptly.

Share a specific example that exhibits both your problem-solving skills and technical expertise. Discuss the context, your thought process in diagnosing the issue, the solution you implemented, and the end results, highlighting lessons learned.

Explain how collaboration is essential in your projects, referring to how you work closely with product managers, data scientists, and other engineers. Illustrate with examples of successful cross-functional teamwork that led to impactful results.

Clarify your experience with both types of databases, including specific technologies you’ve worked with (e.g., MySQL for SQL and MongoDB for NoSQL). Explain scenarios in which you chose one over the other based on project requirements and scalability needs.

Describe your learning process, utilizing resources such as online courses, blogs, community forums, and hands-on projects. Highlight how you proactively stay updated on industry trends and integrate new knowledge into your work.

Discuss how you adapt your communication style based on your audience’s technical proficiency. Share an anecdote that illustrates your ability to simplify and clarify technical jargon, making it accessible and understandable.

Outline your hands-on experience in building data pipelines, including specific tools such as Apache Airflow or AWS Glue. Discuss the importance of efficiency and optimization in your pipeline designs.

Share your approach to task prioritization, mentioning tools or methodologies such as Agile or Kanban. Emphasize your ability to balance urgent needs with long-term project goals while maintaining high-quality work.

Talk about techniques you employ, like caching, database indexing, and partitioning. Provide examples from your past experience where such optimizations led to significant performance improvements.