Runware

Information Technology and Services

PlatformDataEngineer

London, United Kingdom FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid candidates.

The Brief

“Platform Data Engineer at Runware. Skills: Data Engineering, Data Pipelines, Data Modeling, Log Analysis. Build data infrastructure. Optimize data infrastructure”

Industry & Context.

Information Technology and Services
Problems you'll solve

debugging production issues; RCA; performance optimization

Eligibility Requirements

existing right to work in the UK

What They're Looking For.

Must Have

Data Engineer experience, production environment experience, data pipelines, streaming processing, batch processing, data modeling, analytical databases, logs, metrics, platform data, event-based systems, monitoring, observability concepts

Nice to Have

ClickHouse, high-throughput systems, realtime systems, cost monitoring, performance analytics, platform observability, AI, ML platforms, data-heavy products

What You'll Do.

Build data infrastructure

Optimize data infrastructure

Maintain data infrastructure

Support observability

Enable performance analytics

Power internal dashboards

Power customer reporting

Provide structured data

Optimize table layout

Ensure efficient querying

Maintain ingestion pipelines

Build robust pipelines

Implement ETL/ELT workflows

Ensure data reliability

Ensure real-time availability

Build log analysis tooling

Investigate throughput

Investigate bottlenecks

Provide data foundation

Build monitoring tooling

Automate ingestion tasks

Automate cleanup tasks

Build internal libraries

Build debugging utilities

Provide clean data interfaces

Support engineering teams

Contribute to debugging

Contribute to performance optimization

How You'll Work.

Team & Collaboration

Work with Data Expert; Work with platform team; Work cross-functionally; Collaborate with DevOps; Collaborate with ML engineering; Collaborate with backend engineering; Provide data interfaces for Data Expert; Support engineering teams; Contribute to initiatives

Full Job Description

Runware is building a high-performance AI media-creation platform powering instant generation of text, image, video, 3D, and audio. As our platform scales and integrations grow, we need robust, reliable, and high-throughput data systems. We’re looking for a **Data Engineer** to architect and maintain our data backbone — with a special focus on high-volume logs, and performance pipelines. You will work hand-in-hand with our Data Expert and the platform team to transform raw system activity into structured, queryable, high-value data. ### **🎯 Mission** Your main mission is to **build, optimize, and maintain Runware’s data infrastructure**. You will ensure that logs, metrics, performance data, and events are efficiently ingested, processed, stored, and ready to be analyzed by engineering, ML, and product teams. This role is central to: * Supporting observability & platform reliability * Enabling deep log & performance analytics * Powering internal dashboards and customer reporting * Providing clean, structured data to the Data Expert and all stakeholders ### **🧩 What You Will Do** ### **Architecture & Ownership** * 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 ### **Data Engineering & Pipelines** * Build robust pipelines for: * API logs * Model inference logs * Error events * Usage & integration events * 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 ### **Performance & Log Analysis Infrastructure** * Build tooling to support large-scale **log analysis** * Enable deep investigation into latency, throughput, errors, and bottlenecks * Provide the raw data foundation for E2E inference-time monitoring * Help

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