Senior Data Software Engineer
| Company | PsiQuantum |
|---|---|
| Location | Palo Alto, CA, USA, Ontario, Canada |
| Salary | $150000 – $205000 |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s |
| Experience Level | Senior, Expert or higher |
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.