Associate Big Data Engineer

As an Associate Big Data Engineer at GiG, your main responsibilities include working with real-time data aggregation across various products, maintaining and enhancing data processing platforms, collaborating with agile teams and stakeholders in Business Intelligence, Data Science, and Quality Assurance, and utilizing technologies like object-oriented programming in Java and Apache tools for data processing. You'll also support the implementation of best practices in CI/CD and work with Dockerized applications, ensuring smooth data workflows and high performance.

To be considered for the Associate Big Data Engineer role at GiG, you should hold a Bachelor’s Degree in Computer Science or a related field. You should have a solid understanding of object-oriented programming, preferably in Java, with experience in ETL processes using modern technologies like Apache Spark or Apache NiFi. Familiarity with distributed version control systems and a passion for collaborative work in both Linux and Windows environments is also required.

Essential programming skills for an Associate Big Data Engineer at GiG include proficiency in object-oriented programming, particularly with Java, and having experience in functional programming languages such as Python or Scala. Additionally, knowledge of data modeling techniques and experience with low latency databases like ClickHouse will be advantageous for this role.

In the Associate Big Data Engineer role at GiG, you will work with a variety of cutting-edge technologies. This includes real-time data processing tools like Apache Spark or Apache NiFi, object-oriented and functional programming environments, and principles of CI/CD for change management. Familiarity with containerization tools like Docker, as well as a solid understanding of distributed version control systems, will also be crucial for your daily responsibilities.

Working as an Associate Big Data Engineer at GiG offers numerous benefits, including a competitive salary and perks like international health insurance, a comprehensive health and wellbeing package, and opportunities for career development in a supportive environment. You’ll also enjoy flexible working arrangements, such as remote or hybrid options, daily office lunches, and unique perks like a birthday day off and additional time off for personal well-being.

When answering this question, share specific projects where you've implemented real-time data processing techniques. Highlight your experience with tools like Apache Spark or similar technologies, and explain how you optimized the data ingestion process to handle high-volume streams effectively.

Discuss your knowledge of ETL processes and how they have evolved into more efficient methods. You might talk about how you leverage in-memory data transfer techniques instead of traditional ETL, while emphasizing the importance of maintaining data integrity and performance.

Focus on your familiarity with CI/CD tools and techniques. Explain how you've implemented automated testing and deployment pipelines, and discuss the significance of version control systems in managing code changes effectively while ensuring data quality.

Discuss specific instances where you've used Docker to containerize applications. Explain the advantages that Docker brought to your projects, such as simplifying deployment processes and ensuring consistent environments across development and production.

Explain your understanding of data modeling concepts and how you've applied them to different databases. Provide examples of how you've designed schemas for relational databases or document structures for NoSQL databases and the factors that influenced your design choices.

Share your strategies for keeping up with technological advancements, such as following industry blogs, attending webinars or conferences, and participating in online communities. Express your enthusiasm for continual learning and knowledge sharing within teams.

Detail a specific challenge you faced, the thought process behind your solution, and the impact it had on your team or project. Emphasize your problem-solving skills and how you approached the situation methodically.

Discuss your approaches to communication and collaboration. Mention any collaborative tools you've used, your experience with Agile methodologies, and how you ensure all stakeholders are aligned on project goals and deliverables.

Discuss the metrics and tools you use to assess the performance of data applications, including monitoring response times, throughput, and system scalability. Talk about any experience you have with performance tuning based on your evaluations.

Share your passion for data engineering and the aspects of the role that excite you, such as working on innovative projects, solving complex problems, and collaborating with talented professionals to drive impactful results.