Synthetic Data Engineer (Observability & DevOps)

About the Role:

We’re building a large-scale synthetic data generation engine to produce realistic observability datasets — metrics, logs, and traces — to support AI/ML training and benchmarking. You will design, implement, and scale pipelines that simulate complex production environments and emit controllable, parameterized telemetry data.

🧠 What You’ll Do

• Design and implement generators for metrics (CPU, latency, throughput) and logs (structured/unstructured).
• Build configurable pipelines to control data rate, shape, and anomaly injection.
• Develop reproducible workload simulations and system behaviors (microservices, failures, recoveries).
• Integrate synthetic data storage with Prometheus, ClickHouse, or Elasticsearch.
• Collaborate with ML researchers to evaluate realism and coverage of generated datasets.
• Optimize for scale and reproducibility using Docker containers.

✅ Who You Are

• Strong programming skills in Python.
• Familiarity with observability tools (Grafana, Prometheus, ELK, OpenTelemetry).
• Solid understanding of distributed systems metrics and log structures.
• Experience building data pipelines or synthetic data generators.
• (Bonus) Knowledge of anomaly detection, time-series analysis, or generative ML models.

💸 Pay $50 - 75/hr depending on experience Remote, flexible hours Project timeline: 5-6 weeks