Senior Big Data Engineer - job 1 of 2

As a Senior Big Data Engineer at Nexxen, your key responsibilities include implementing and maintaining high-scale Big Data solutions, owning the end-to-end feature development process from design to production, and collaborating with various stakeholders like product managers and data providers. You will also research cutting-edge technologies to enhance the scalability and performance of our data applications.

Candidates for the Senior Big Data Engineer position at Nexxen should possess a minimum of 7 years of engineering experience, with at least 4 years specifically in data engineering. Proficiency in Java is essential, with skills in Python and Scala being advantageous. Knowledge of technologies such as Hadoop, Spark, and various SQL and NoSQL databases is also required.

The culture for a Senior Big Data Engineer at Nexxen is characterized by a commitment to collaboration, curiosity, and a customer-centric mindset. We embrace individuality and value diverse experiences, encouraging you to bring your unique perspective to the table while working harmoniously with your team to achieve our goals.

At Nexxen, the salary for the Senior Big Data Engineer role ranges from $160,000 to $200,000, with additional potential for incentives or bonuses. Actual compensation may vary based on experience, skills, and geographic location, ensuring transparency and equity in pay.

A Senior Big Data Engineer at Nexxen should be familiar with a variety of technologies including but not limited to Hadoop, Spark, Kafka, and various SQL and NoSQL databases like MongoDB and Redis. Experience with AWS services, Linux, Docker, and container orchestration tools like Kubernetes would also be beneficial.

Highlight specific projects you've worked on, focusing on the technologies you used, your decision-making process, and the outcomes achieved. Discuss your approach to scalability and how you ensured performance in your previous roles.

Share your knowledge of ETL tools and frameworks you've used. Discuss how you planned and executed ETL pipelines, and how you addressed challenges regarding data quality, transformation, and performance.

Explain your strategies for data validation and error handling within ETL processes. Highlight any tools you've used for tracking data quality and how you implement remedial measures to address issues.

Discuss your experience with Java primarily, detailing specific applications or systems you've built. If you have knowledge of Python or Scala, explain how you've utilized these languages in data processing tasks or algorithm implementation.

Provide details of a project including the key challenges you faced, the technologies you implemented, and the impact of the project. Be sure to highlight your problem-solving skills and teamwork.

Discuss your experiences working with product managers, engineers, and other stakeholders. Emphasize your communication skills, your flexibility in adapting to different viewpoints, and how you ensure alignment on project goals.

Highlight any relevant projects where you utilized AWS services. Discuss specific services you've worked with, such as EC2 or Kinesis, and how you integrated them into your Big Data architecture.

Explain your methods for staying informed about industry trends and technological advancements. You might mention attending conferences, participating in online forums, or contributing to open-source projects.

Discuss your familiarity with tools like Kubernetes and how they benefit your data engineering workflows. Touch on aspects like deployment, management of application containers, and scaling applications as needed.

Detail your approach to troubleshooting and resolving production issues, including your methods for monitoring system performance and utilizing logging systems. Share an example of how you effectively managed an incident.