Data Engineer Infrastructure
ParisFull-TimeMid-levelSoftware Engineering
Responsibilities:
- Design and implement a robust data storage and retrieval system for calibration results, error logs, and performance metrics.
- Develop pipelines to automatically collect, normalize, and index calibration outputs for easy querying and meta‑analysis.
- Build tools and APIs that allow scientists and engineers to quickly answer operational questions (success rates, failure points, drift statistics).
- Implement time‑series analysis frameworks to track calibration dynamics, detect anomalies, and generate reports.
- Establish standards for data schemas, provenance, retention, and reproducibility of calibration results.
- Provide visibility through automated reporting on calibration performance, hardware reliability, and analysis quality.
- Mentor engineers and contribute to long‑term strategy for calibration data infrastructure.
Requirements:
- 5+ years experience in backend engineering, data infrastructure, or DevOps with production systems.
- Strong proficiency in Python and experience with data engineering frameworks (Pandas, SQLAlchemy, Spark, or equivalent).
- Expertise in time‑series databases (TimescaleDB, InfluxDB, Prometheus) and log aggregation systems (ELK stack, Grafana, or similar).
- Proven track record in designing scalable data pipelines and APIs for scientific or hardware‑related data.
- Experience with observability stacks (metrics, logs, traces) and building dashboards for technical users.
- Familiarity with statistical analysis and anomaly detection; ability to collaborate with scientists on model integration.
- Strong understanding of CI/CD, testing, and reproducibility in scientific or hardware‑in‑the‑loop environments.
- Excellent communication skills and ability to translate operational needs into technical solutions.
