As the Engineering Manager for Observability, you will lead a team of five engineers responsible for shaping the future of visibility and efficiency at Figma. Lead and grow a team of engineers responsible for the reliability, scalability, and evolution of Figma’s observability and cost engineering platforms Own and operate Figma’s core observability stack, including vendor platforms such as Datadog, ensuring high availability, strong data quality, and effective signal-to-noise across metrics, logs, and traces Define and drive the technical strategy for instrumentation standards, observability libraries, agents, and operators used to monitor internal and external facing services Explore and implement innovative, AI-driven approaches to anomaly detection, root cause analysis, signal correlation, and operational automation Establish clear frameworks for cost attribution, budgeting, forecasting, and alerting across infrastructure and observability spend, enabling teams to make informed tradeoffs Partner with infrastructure, product engineering, finance, and security teams to improve visibility into system health and cost efficiency at scale Lead initiatives to optimize observability footprint and spend, balancing depth of insight with performance and cost considerations Coach and mentor engineers through career development, performance feedback, and technical leadership, fostering a culture of ownership, collaboration, and high quality execution

The Solutions Architect (SA) is a strategic partner to the Sales team, responsible for uncovering complex supply chain inefficiencies and designing tailored integration solutions. You will lead the technical discovery, build high-impact Proof of Concepts (POCs), and serve as the subject matter expert on how our platform integrates customer ecosystems and orchestrates B2B processes to drive resilience and scalability. Function as the technical lead and trusted advisor through the sales cycle Conduct discovery sessions directly with prospective and existing customers to gather business and technology requirements Translate technical architecture into business outcomes for C-suite stakeholders (CTO, COO, VP of Supply Chain) Demonstrate the value of Cleo solutions through presentations and demonstrations on-site and remote Collaborate with Product Management to provide 'voice of the customer' feedback, influencing the product roadmap based on market trends and prospect requirements Assess, scope, and complete proofs of concept (POCs) to demonstrate the value of Cleo’s solutions in a customer environment Lead the technical response for RFI/RFPs, ensuring our unique value propositions are clearly articulated against competitor weaknesses

• Research target accounts and contacts to set quality meetings and create pipeline opportunities. Target new accounts through outbound calling efforts and by creating and executing strategic email and social campaigns • Qualify potential customers to gather information about their business strategy, goals, time series data challenges, and current technology used • Manage and nurture quality leads by sharing Seeq content (blogs, whitepapers, webinars, etc.) • Collaborate with Sales to strategize the best ways to prospect into target accounts and contribute to account plans. Share feedback on messaging and tactics • Follow-up on inbound leads to create meetings and opportunities • Track activities (email, calls, LinkedIn touches), MQLs, contacts, and account information in CRM • Apply effective communication skills to collaborate with cross-functional teams • Work in a fast-paced environment and take the initiative to hit monthly targets, try new things, and amplify your successes by sharing your findings with the broader team

We are seeking a highly motivated and talented Data Engineer I (Analytics Engineering) to help build and scale Capital Rx’s analytics foundation. This role focuses on developing high-quality, analytics-ready datasets and a consistent metrics/semantic layer that power reporting, self-service BI, and operational insights. Partner closely with operational and analytics teams to translate business needs into analytics-ready data models, curated data marts, and reusable datasets. Build and maintain dbt projects (staging → intermediate → marts), including model layering, macros, exposures, packages, and source freshness to accelerate delivery and improve reliability. Design and evolve dimensional models (facts/dimensions, grain, SCD patterns) and analytics patterns aligned with key business domains. Own metrics definitions end-to-end: establish KPI logic, naming standards, calculation rules, and governance; maintain a metrics/semantic layer so stakeholders use consistent definitions across tools and teams. Implement robust data quality practices (dbt tests, constraints, anomaly checks/monitoring) to ensure reliable downstream reporting and decision-making. Produce clear documentation and enablement assets (dbt docs, lineage, data dictionaries, metric definitions, onboarding guides) to support self-service analytics. Optimize warehouse performance and cost in Snowflake through SQL tuning, incremental models, appropriate materializations, and efficient data design. Use disciplined software engineering practices (Git-based workflows, peer review, CI checks, versioning, environment promotion) to deploy changes safely and repeatably. Proactively identify and resolve data-related issues, ensuring system reliability and data integrity. Provide ongoing support for critical analytics data products and participate in an on-call rotation as needed. Responsible for adherence to the Capital Rx Code of Conduct including reporting of noncompliance.