Data Engineer

The company operates in the clinical research, healthcare technology, and life sciences sectors, focusing on the development of scalable data infrastructure for next-generation AI systems.

• Design, develop, and maintain scalable data pipelines and ETL processes for AI and machine learning workloads;
• Build and optimize data models, feature-ready datasets, and semantic layers for analytics and AI applications;
• Collaborate with AI and engineering teams to ensure high-quality, reliable, and accessible data;
• Implement data governance, security, monitoring, lineage tracking, and observability frameworks;
• Optimize cloud-based data platforms for scalability, reliability, and cost efficiency;
• Develop streaming and event-driven data pipelines for real-time AI applications;
• Design storage and retrieval systems for vector databases, knowledge graphs, and AI artifacts;
• Implement automated data validation, schema testing, and quality assurance processes;
• Monitor and troubleshoot data pipelines to ensure operational reliability;
• Stay current with emerging data engineering, cloud, and AI technologies.

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field;
• 5+ Years of data engineering experience, including at least 2 years supporting AI or machine learning infrastructure;
• Strong programming skills in Python and Scala;
• Experience with SQL, NoSQL, and distributed computing frameworks;
• Experience with cloud platforms (AWS, Azure, or GCP), Docker, Kubernetes, Terraform, and modern orchestration tools such as Airflow;
• Knowledge of streaming technologies, data lakehouse architectures, vector databases, and AI data workflows;
• Strong problem-solving, collaboration, and technical leadership skills, with experience mentoring engineers and contributing to architecture decisions;
• Nice to have: Spark, Kafka, Flink, feature stores, RAG systems, LLM data processing, MLOps.

• No conditions specified