Senior Data Software Engineer

Summary

Join PsiQuantum's Quantum Applications Software Team as a Senior Data Software Engineer. You will architect, build, and maintain scalable data pipelines supporting critical quantum software applications. This hands-on role involves collaborating with scientists, gathering requirements, and building foundational data infrastructure. You will champion engineering excellence and work with cross-functional teams. The position requires expertise in data engineering, cloud technologies, and programming languages. Experience with high-performance computing and large-scale scientific computations is essential.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• 8+ years in Data Engineering with hands-on cloud and SaaS experience
• Proven experience designing data pipelines and workflows, preferably for high-performance or large-scale scientific computations
• Strong knowledge of database design principles (relational and/or NoSQL) for complex or high-volume datasets
• Proficiency in one or more programming languages commonly used for data engineering (e.g., Python, C++, Rust)
• Hands-on experience with orchestration tools such as Prefect, Apache Airflow, or equivalent frameworks
• Hands-on experience with cloud data services, e.g. Databricks, AWS Glue/Athena, AWS Redshift, Snowflake, or similar
• Excellent teamwork and communication skills, especially in collaborative, R&D-focused settings

Responsibilities

• Develop and refine data processing pipelines to handle complex scientific or computational datasets
• Design and implement scalable database solutions to efficiently store, query, and manage large volumes of domain-specific data
• Refactor and optimize existing codebases to enhance performance, reliability, and maintainability across various data workflows
• Collaborate with cross-functional teams (e.g., research scientists, HPC engineers) to support end-to-end data solutions in a high-performance environment
• Integrate workflow automation tools, ensuring the smooth operation of data-intensive tasks at scale
• Contribute to best practices for versioning, reproducibility, and metadata management of data assets
• Implement Observability: Deploy monitoring/logging tools (e.g., CloudWatch, Prometheus, Grafana) to preempt issues, optimize performance, and ensure SLA compliance

Preferred Qualifications

• Knowledge and experience with containerization and orchestration tools such as Docker and Kubernetes and event-driven architectures
• Knowledge of HPC job schedulers (e.g., Slurm, LSF, or PBS) and distributed computing best practices is a plus
• Experience with Infrastructure as Code (IaC) tools like Terraform, AWS CDK, etc
• Deployed domain-specific containerization (Apptainer/Singularity) or managed GPU/ML clusters

Benefits

Equal employment opportunity for all applicants and employees