At Classiq Technologies, we’re shaping the future of quantum computing.

Our mission is to make quantum computing practical and impactful by bridging the gap between hardware and real-world applications. Our platform enables developers and enterprises to design, optimize, and deploy quantum algorithms at scale, automating complex processes and supporting teams from beginners to experts across diverse architectures.

Clasiq is trusted by Fortune 500 leaders and global governments. We accelerate the quantum era with a platform that transforms bold quantum ideas into working applications.

We are seeking a Data Engineer with a passion for building data platforms from the ground up to join our Engineering team and help us develop and scale our data collection solutions. In this role, you will play a crucial part in establishing the infrastructure that supports quantum data for machine learning, software engineering, and business intelligence initiatives. You’ll develop, deploy, and maintain the data pipelines and workflows required to collect, process, and visualize data across the organization.

• Lead the design and development of scalable, efficient data warehouse and business intelligence solutions aligned with organizational goals.
• Build and maintain data pipelines to support real-time and batch processing, ensuring efficient data storage, retrieval, and transformation.
• Design and optimize data models, schemas, and database structures for analytics and reporting.
• Leverage DevOps and infrastructure skills to build and support data systems.
• Implement monitoring and observability tools to ensure system reliability.
• Implement and maintain data quality checks and monitoring mechanisms to ensure accuracy and reliability across the data lifecycle.

• 5+ years of experience in data engineering, with strong expertise in building and maintaining data pipelines and data warehouses.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and related services for data storage, processing, and orchestration.
• Solid understanding of ETL/ELT workflows, data modeling, and database design principles.
• Proficiency in Python for data processing and automation.
• Hands-on experience with DevOps tools — CI/CD pipelines, Docker, Kubernetes, and infrastructure-as-code (Terraform).
• Strong background in SQL and experience with modern data storage systems (e.g., Snowflake, BigQuery, or similar).
• Familiarity with data quality frameworks, observability, and monitoring tools.
• Excellent problem-solving skills and the ability to optimize data workflows for both batch and real-time environments.
• Strong communication and collaboration skills, with the ability to work cross-functionally with developers, ML engineers, and data analysts.
• A self-starter mindset with enthusiasm for learning new technologies and improving existing systems.