• Gather requirements to build and maintain data analyses, risk triggers / rules, visualizations and interactive dashboards that will drive business decisions and risk monitoring (in BI tools, web analytics platforms, and excel) • Facilitate the analytical needs of the business, collaborate in the design of diagnostic solutions, and deliver outstanding data products and insights • Use internal and external analytics tools, SQL, and scripting languages to access and analyze multivariate data • Perform deep analyses to help us understand our data better and help build out robust risk management and monitoring tools and reports for the business • Use emerging tech and tools to evaluate trends and develop actionable insights • Use data to independently uncover problems and business opportunities • Support data migrations in order to automate repeatable tasks and KPI tracking throughout the business • Track and analyze performance of various business and risk segments and programs to evaluate effectiveness • Build analysis, data visualizations, and multi-functional reporting that provides critical support to the operations and decision-making of the organization • Help translate business functional requirements into technical specifications and, conversely, technical approaches into easily digestible terms • Provide analytics expertise to key business partners regarding data & analytics • Support Commercial, Marketing & Risk teams with process improvements for optimized delivery of diverse data requests • Support business partners in adopting reporting solutions and business intelligence tools for self-service • Write LookML and SQL queries and build dashboards in Looker • Systematically identify issues with data quality and help find solutions to address • Assist with ad-hoc projects and tasks as necessary

About Runware Runware is building a high-performance, full-stack AI media-creation platform — empowering developers and companies to generate any type of media instantly. As we scale fast and integrate increasingly complex models, we need stronger visibility, analytics, and monitoring across the whole platform stack. We’re looking for a Data Expert (Analytics + Monitoring + Observability) to help us better understand, measure, and optimize how the Runware platform performs at scale — internally and for our clients. 🎯 Mission Your main goal is to give Runware full visibility over: - End-to-end inference performance - Integration usage and model activity - Errors, delays, bottlenecks, regressions - Internal and client-facing analytics dashboards - Health and performance of production pipelines You will provide the data insights that allow engineering, ML, backend, DevOps, and leadership to make informed decisions — and to continuously improve performance and reliability. 🧩 What You Will Do Performance Monitoring & Benchmarking - Build and maintain E2E inference time tracking (global and per-model). - Monitor how implementation changes impact total request latency. - Detect regressions introduced by suboptimal code paths. - Provide automated alerts & historical trends. Usage & Analytics Reporting - Build dashboards for internal use (engineering, product, leadership). - Provide client-facing usage dashboards (requests, errors, success rate, performance). - Support clients who need visibility to debug their integrations. - Track model-level usage, API endpoints usage, adoption metrics, etc. Platform Observability - Implement metrics, logs, and traces that help the entire platform scale smoothly. - Work closely with DevOps & backend teams to improve system observability. - Provide insights that guide infra decisions (GPU allocation, autoscaling, caching, batching, etc.). Data Infrastructure Ownership - Select and maintain tooling (e.g., Prometheus/Grafana, Datadog, OpenTelemetry, ELK, BigQuery, etc.). - Ensure data pipelines are reliable, accessible, and always up-to-date. - Build simple, easy-to-read dashboards for both technical and non-technical teams.

• Transform complex datasets into actionable insights • Build and maintain analytics infrastructure • Partner with cross-functional teams to drive data-informed decision-making and product improvements • Own the end-to-end analytics lifecycle—from data modeling and dashboard creation to experimentation and KPI development

We are looking for a talented Quantum - Data Conversion Lead to join our team in Systems/Information Technology from your remote home office. In this role, you will make an impact in the following ways:  Own the outcome - Be the single owner for data readiness for each release, country, and plant—clean, complete, and on time. - Define the data conversion strategy, standards, and guardrails, and ensure consistent adoption. Plan & prioritize - Own the end‑to‑end data conversion plan (objects, cycles, dependencies, milestones). - Prioritize the conversion backlog with Build, Functional, and Test leads. - Align data loads with environment refreshes and testing cycles (SIT, E2E, UAT, PAT). Lead day‑to‑day execution - Review overnight data loads and conversion results; triage failures and data quality issues. - Direct daily work for data engineers and vendor SI partners to resolve root causes. - Ensure mappings, transformation rules, and reconciliation steps are accurate and repeatable. Coordinate across tracks - Partner with Integration Leads to align interfaces and conversions (structures, timing, cutovers). - Collaborate with Functional Leads to validate business rules and required data shapes. - Work with Testing teams to ensure clean, stable data is available and defects tied to data are resolved. Design governance - Lead reviews of conversion designs, mappings, and exception handling. - Enforce global standards for reuse, automation, error handling, and auditability. - Approve design changes that impact downstream processes, performance, or compliance. Quality & controls - Define and enforce Definition of Ready / Definition of Done for conversion objects. - Set and monitor data quality thresholds (accuracy, completeness, duplicates, referential integrity). - Ensure reconciliation and business sign‑offs are completed for each release. Issue management & decisioning - Make day‑to‑day trade‑off decisions (fix at source vs. transform vs. post‑load correction). - Escalate issues only when schedule, cost, compliance, or scope is materially impacted. - Drive root‑cause analysis for recurring issues and institutionalize permanent fixes. Cutover readiness - Own the data conversion runbook, timing, staffing, and contingency plans. - Validate mock conversions and ensure cutover rehearsals meet timing and quality targets. - Lead data activities during go‑live and support rapid stabilization. Stakeholder communication - Provide clear, concise status updates—what’s on track, what’s at risk, and recommended actions. - Translate technical data issues into business impact. - Communicate readiness, risks, and mitigation plans to program and leadership teams. People & partner leadership - Coach data engineers on scalable, reusable conversion patterns. - Manage vendor SI partners to outcomes and delivery commitments. - Build internal capability over time to reduce long‑term dependency on external partners. Compliance & auditability - Ensure data conversions comply with security, privacy, and retention requirements. - Maintain end‑to‑end data lineage and traceability from source to SAP. - Keep documentation current (mappings, rules, reconciliation templates, approvals). Continuous improvement - Industrialize the data conversion factory through automation and standardization. - Track KPIs such as pass rates, reconciliation variances, and cycle times. - Capture lessons learned and scale improvements globally. Cummins is an equal opportunity employer. Our policy is to provide equal employment opportunities to all qualified persons without regard to race, sex, color, disability, national origin, age, religion, union affiliation, sexual orientation, veteran status, citizenship, gender identity, or other status protected by law.