Senior Data Engineer, Preply

• Bachelors or Masters Degree in Computer Science or Engineering (or equivalent work experience)
• Previous experience as a Senior Data Engineer or in a similar role is essential
• Strong coding skills in languages like Python or Scala, coupled with proficient SQL experience. Experience with distributed bulk data processing frameworks like Spark or Presto is required
• Previous experience in setting and scaling DataBricks in a medium-sized organization is a must
• Experience working with MLOps tooling, such as Feast for a feature store, MLFlow for experiment tracking, and model registry
• Cloud Expertise. Proficiency in maintaining cloud-based, high-volume distributed data processing platforms, such as AWS or GCP
• Kubernetes Proficiency. Ability to deploy and support tooling on Kubernetes
• Strong curiosity, problem-solving abilities, and a knack for identifying and addressing complex issues
• Innovative Mindset. Creative thinking and a proactive attitude toward devising and evaluating new solutions
• Excellent written and verbal communication skills in English are a must

Desirable

• Participate in critical architectural decisions, evaluating multiple approaches to empower our people to inform their decisions with quality data.
• You will heavily contribute to selecting the appropriate tech stack and defining new data/ML standards
• Enable DS/ML Teams: Assist data science and machine learning teams in building and operationalizing their critical ML models, providing guidance and expertise throughout the process
• Develop the MLOps framework: Make sure the ML capabilities of the company are extended to multiple teams, enabling reusability and standardization through an effective MLOps framework
• Scaling and Evolving Infrastructure: Enhance our multi-tiered data infrastructure, focusing on cost efficiency, performance optimization, scalability, and reliability
• Designing Future-Ready Solutions: Develop and implement innovative strategies to meet current requirements while anticipating the evolving needs of our internal ML platform
• Mentoring Data Engineers: Serve as a mentor to fellow Data Engineers, fostering their professional growth and skill development
• Collaborating with the rest of the Data Platform: Work closely with our existing Data Platform team to ensure seamless integration and expansion of our data models and unlock new use cases
• Enhancing Data Processing: Improve data ingestion and processing capabilities in our data lake, data warehouse, and ML platform
• Implementing Data Quality Measures: Implement data quality tools and processes to ensure the integrity and reliability of our data assets