Senior Big Data Engineer

As a Senior Big Data Engineer at GiG, your key responsibilities include collating data from various real-time sources, maintaining and enhancing a platform that ingests thousands of messages per second, and working closely with agile teams to support product objectives. You’ll also collaborate with Business Intelligence, Data Science, and Quality Assurance teams, all while following best practices for delivering high-quality code and product increments.

To qualify for the Senior Big Data Engineer position at GiG, you should have a Bachelor’s Degree in Computer Science or a related field. Essential skills include proficiency in object-oriented programming (Java), data modeling, and experience with ETL processes, specifically with tools like Apache Spark or Apache NiFi. Familiarity with CI/CD practices, Docker, and working in both Linux and Windows environments is also vital.

In the Senior Big Data Engineer role at GiG, you'll work with a variety of technologies including Java for object-oriented programming, Apache Spark or Apache NiFi for data processing, and Docker for application containerization. Additionally, you'll be involved in using low-latency databases like ClickHouse, as well as engaging with continuous integration and delivery practices in your workflow.

As a Senior Big Data Engineer at GiG, you’ll enjoy numerous benefits including a fully remote or hybrid working model, international health insurance, and a health and wellbeing package worth 350 EUR per year. Additional perks include a dedicated day off for your birthday, a 'Me Time' day off every year, and free lunches in the office. Career development opportunities are plentiful, allowing you to grow alongside some of the best talent in the industry.

Key skills for the Senior Big Data Engineer position at GiG include proficiency in Java for object-oriented programming, adaptability with functional programming languages like Python or Scala, and a solid understanding of data modeling and ETL processes. It’s also important to have an analytical mindset, effective collaboration abilities, and mentorship skills to guide junior engineers in the team.

When answering this question, highlight specific projects or tasks where you used Apache Spark. Discuss how you harnessed its capabilities to handle large datasets, emphasizing your understanding of its architecture, processing speed advantages, and any specific features you utilized such as Spark SQL or MLlib.

In your response, discuss your methodology for data modeling, emphasizing the importance of scalability and efficiency. Mention specific tools or techniques you’ve used, and provide examples of projects where your data modeling significantly improved application performance or reduced load times.

Talk about your hands-on experience with traditional ETL processes and how you’ve transitioned to modern techniques such as in-memory processing. Provide examples of technologies you’ve used, like Apache NiFi, and explain how these adaptions improved workflow and data handling efficiency.

Share a specific experience where your participation in an agile team had a measurable impact. Discuss your role, how you collaborated with team members, and any methodologies you applied that helped the team meet its objectives efficiently.

Discuss your approach to maintaining high standards of code quality, mentioning practices like code reviews, unit testing, and integration testing. Highlight your use of version control systems as part of your development workflow and how these practices contribute to a successful project.

Explain your understanding of microservices and specific strategies you've employed to enhance data flow, such as implementing event sourcing, using message queues, or optimizing API calls. Use examples of how these strategies improved system performance in past projects.

Talk about any relevant experience with ClickHouse or similar databases. Detail how you’ve utilized their features for real-time data processing and analytics, and share specific scenarios where you optimized query performance or improved data retrieval times.

When addressing this question, share insights on your approach to data migrations, mentioning specific tools and techniques you’ve employed. Focus on a particular challenge you faced and the actions you took to resolve it, demonstrating your problem-solving skills.

Discuss your familiarity with CI/CD pipelines, mentioning any tools such as Jenkins or GitLab CI that you've used. Share how these practices have helped you automate deployments, improve code integration processes, and maintain healthy development cycles.

Highlight your mentoring experience by discussing your approach to training and supporting junior developers. Share specific strategies you’ve utilized, such as pair programming, code reviews, or regular check-ins, illustrating how this has positively impacted team dynamics and individual growth.