Platform Data Engineer

Solid experience as a Data Engineer or similar role in a production environment. Strong understanding of data pipelines, streaming vs batch processing, and data modeling. Experience working with analytical databases (ClickHouse is a plus, but not mandatory). Comfortable digging through logs, metrics, and platform data to understand system behavior. Familiarity with event-based systems, monitoring, and observability concepts. Pragmatic mindset: care about usefulness, reliability, and performance over theory. Comfortable working cross-functionally with backend, infra, and data profiles. Startup / scale-up experience is a plus. Experience with high-throughput or realtime systems is a plus. Exposure to cost monitoring, performance analytics, or platform observability is a plus. Background in AI, ML platforms, or data-heavy products is a plus.

Build, optimize, and maintain Runware’s data infrastructure. Ensure logs, metrics, performance data, and events are efficiently ingested, processed, stored, and ready for analysis. Design, build, and maintain schemas and data models. Optimize table layout, partitioning, indexing, and compression for high-volume data. Ensure fast, efficient querying for logs, requests, metrics, and performance traces. Maintain ingestion pipelines for billions of records. Build robust pipelines for API logs, model inference logs, error events, usage & integration events, and GPU & system metrics. Implement ETL/ELT workflows to transform raw data into analytics-ready structures. Ensure quality, reliability, and real-time availability of data sources. Build tooling to support large-scale log analysis. Enable deep investigation into latency, throughput, errors, and bottlenecks. Provide raw data foundation for E2E inference-time monitoring. Help debug production issues using logs and traces. Work closely with DevOps, ML, and backend engineering. Integrate pipelines with monitoring tools (Prometheus, Grafana, Datadog, OpenTelemetry). Automate ingestion and cleanup tasks. Build internal libraries or utilities to support monitoring and debugging workflows. Provide clean data interfaces for the Data Expert. Support engineering teams by exposing the right logs and metrics. Contribute to debugging, RCA, and performance optimization initiatives.