Big Data Developer / Machine Learning Engineer
Are you passionate about new technologies and looking to be part of a software development team that is constantly striving for innovation in a warm, relaxed, and stimulating environment?
We are looking for a Big Data Developer / Machine Learning Engineer to join our “Audience” team.
Our success depends on our ability to tackle challenges together. We are technology enthusiasts. Always on the lookout for the best tools and methods to do our work, we also place great importance on communication skills and team spirit.
If you’re interested in building Business Intelligence tools that leverage data generated by millions of audio streaming users, read on!
You’re an excellent candidate if you:
- Are proficient in programming languages like Scala (a must) and Python (an asset).
- Have already developed applications that prioritize high concurrency and short response times.
- Have mastered data modeling and parallel processing concepts, and have solid knowledge of Big Data technologies (e.g., Apache Spark) and distributed data storage systems (e.g., Scylla DB).
- Have advanced knowledge of containerization tools (Docker, Kubernetes, Red Hat Openshift) and cloud platforms (e.g., AWS).
- Have experience with test-driven development (TDD) and are familiar with CI/CD tools such as GitHub Actions and Bamboo.
- Have already designed and optimized data processing pipelines using tools like Airflow.
- Have proven experience in Machine Learning engineering and operations applied to Big Data, and have worked with frameworks like Apache Spark for ML.
- Use AI tools in your software development work.
- Are autonomous and possess excellent problem-solving skills and analytical thinking.
- Have developed in Agile/Scrum environments.
- Have at least five years of professional experience as a Big Data developer.
Your responsibilities:
- Develop new products/applications and enhance existing ones using Scala or other relevant technologies.
- Participate in all phases of the software development lifecycle: planning, design, coding, testing, deployment, monitoring, support, documentation.
- Write high-quality code to develop and maintain robust, large-scale applications.
- Work closely with data scientists to understand their models and translate complex algorithms into scalable solutions.
- Manage the lifecycle of machine learning models, including testing, deployment, and monitoring in production environments.
- Maintain documentation of processes and best practices, so team members can easily understand and follow established protocols.
- Collaborate cross-functionally with Big Data developers, Data Ops teams, and IT Ops to ensure seamless integration of ML solutions into the existing infrastructure.
- Be an engaged and positive member of a self-organized, multidisciplinary Agile team.
- Offer help and be open to asking for it in return.
- Be receptive to others’ ideas while communicating respectfully.
- Work with the Product Manager and other product teams to help define the future solutions of the organization.
- Recommend technological choices and be capable of supporting them.