• Transform data into actionable insights that support revenue growth, operational efficiency, and strategic planning. • Identify and contribute to the development of scalable data tools that improve how data is transformed, managed, and accessed—reducing time to insight. • Build predictive and forecasting models to support revenue growth, marketing effectiveness, and improved customer experiences. • Lead analytics and modeling projects that translate organizational data into clear, data-driven recommendations. • Design and maintain interactive dashboards and reports aligned to key performance indicators (KPIs). • Implement modern tools and techniques to analyze, visualize, and communicate data trends effectively. • Partner with business teams to support operational improvements and growth initiatives, defining measurable outcomes. • Prepare both executive-level summaries and detailed analytical reports, including clear written insights and visual storytelling. • Present findings and recommendations to senior leadership, including options and trade-offs. • Collaborate closely with Finance and executive stakeholders to support recurring business review and planning cycles. • Contribute to additional analytics initiatives as business needs evolve.

Role 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. 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 debug production issues using logs and traces - Tooling & Observability Infrastructure - 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 - Collaboration & Cross-Functional Support - Provide clean data interfaces for the Data Expert (dashboards, monitoring, analytics) - Support engineering teams by exposing the right logs and metrics - Contribute to debugging, RCA (root cause analysis), and performance optimization initiatives Qualifications - 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: you 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 Nice to Have - Experience with high-throughput or realtime systems - Exposure to cost monitoring, performance analytics, or platform observability - Background in AI, ML platforms, or data-heavy products Benefits - Generous paid time off – vacation, sick days, public holidays - Meaningful stock options – share in the upside you create - Remote-first setup – work from home anywhere we can employ you - Flexible hours – own your schedule outside core collaboration blocks - Family leave – paid maternity, paternity, and caregiver time - Company retreats – twice-yearly gatherings in inspiring locations Company Description We’re a remote-first collective, meeting in person twice a year to plan, brainstorm, celebrate wins, and enjoy some face-to-face time. We have core hours for cooperative working and calls, but outside of that your calendar is yours. Work the hours that let you perform at your peak while also building a healthy life. Our release cycles are fast and intense, but they’re followed by real downtime. After big pushes we expect the team to unplug, recharge, and come back ready & stronger than ever for the next leap. Please note: We are unable to offer visa sponsorship in the UK at this time. Candidates must have existing right to work in the UK.

• Designing, developing, and maintaining scalable data pipelines and ETL/ELT processes • Contributing to the design and implementation of a greenfield data platform • Operating in a hybrid greenfield and brownfield environment • Leading development discussions and defining engineering patterns and standards • Assessing and analyzing legacy data processes • Collaborating with cross-functional teams • Improving data pipeline performance, observability, and reliability • Supporting documentation efforts for scalability and platform adoption • Providing technical mentorship and guidance to junior engineers • Acting as a technical SME in internal and client-facing discussions

• Design, implement, and operate production data platforms across Azure SQL, on premise SQL Server, Azure Storage, Microsoft Dataverse, and Microsoft Fabric or Power BI. • Build and maintain ingestion pipelines using SFTP file drops, APIs, CDC or SQL Log Shipping patterns, and Azure Data Factory or equivalent orchestration tools. • Design and evolve analytic data models using star, snowflake, Data Vault, or hybrid approaches. • Implement automated data quality checks, validations, and alerts across pipelines and semantic layers. • Implement practical data governance using Microsoft Purview or equivalent tooling. • Partner closely with DevOps to reduce manual processes and increase repeatability.