Role Description As a Zuora Enterprise Solution Architect (ESA), you will be a trusted partner and advisor to live, already-implemented customers. This role is focused on maximizing the value of Zuora solutions by aligning them with customers’ strategic goals, optimizing Order-to-Revenue operations, and driving measurable business outcomes. You will deliver customized roadmaps, lead strategic workshops, and provide ongoing optimizations to ensure that Zuora’s platform continues to meet customers’ evolving needs. Acting as a key strategist, you’ll guide customers through challenges and opportunities, helping them unlock the full potential of Zuora to support their long-term growth and success. Key Responsibilities - Strategy Alignment: - Collaborate with customers to understand their business objectives and develop phased, actionable roadmaps tailored to their strategic goals. - Design scalable solutions that evolve with customers’ growth, ensuring long-term value creation and adaptability to changing business needs. - Prioritize frameworks that deliver measurable outcomes and align with best practices in subscription management and Order-to-Revenue processes. - Strategic Advisory & Insights: - Serve as a trusted advisor, offering actionable insights into industry trends, best practices, and process optimizations. - Provide strategic guidance to help customers leverage Zuora for operational agility, enhanced efficiency, and competitive advantage. - Proactively identify opportunities for innovation and improvement, ensuring customers continuously extract value from Zuora. - Workshops & Optimization: - Lead standardization workshops to assess and optimize configurations, workflows, and customizations, establishing scalable and efficient frameworks. - Facilitate detailed workshops to refine configurations, improve system performance, and streamline processes, delivering tailored recommendations to maximize ROI and user satisfaction. - Lead technical configuration and integration reviews and provide optimization recommendations. Qualifications - 5+ years of hands-on proficiency in Zuora Billing, Revenue, and CPQ modules, with expertise in designing scalable, impactful solutions. - 10+ years of Domain-Specific Expertise: Experience with Billing and Revenue Management and ERP platforms such as SAP, Oracle, and NetSuite. - Proven ability to deliver customer-aligned roadmaps and provide strategic advisory that aligns with evolving business needs. - Integration Proficiency, strong understanding of REST and SOAP APIs, and ETL, with experience integrating with ERP, CRM, and core systems. - Exceptional strategic thinking, analytical, and communication skills with a customer-first approach. Preferred Skills - Advanced Zuora Certifications: Strong hands-on proficiency in Zuora Billing, Revenue, and CPQ modules, supported by certifications like Zuora Billing Certified Consultant and Zuora Billing Delivery Architect. - Compliance Knowledge: Familiarity with GDPR, FISMA, ASC 606, and IFRS 15 compliance standards and their impact on revenue recognition. - Diverse Industry Experience: Ability to adapt solutions across various sectors, particularly in subscription management industries. - Global Project Leadership: Experience managing complex, multi-stakeholder projects in international and cross-cultural settings. - Process Optimization: Expertise in leading workshops for standardization, configuration refinement, and process improvements to maximize ROI and efficiency. - Strategic Advisory Skills: Ability to provide actionable insights into industry trends, best practices, and operational strategies for long-term customer success. - Independent Consulting Acumen: Proven track record of self-directed consulting engagements, delivering results with minimal supervision. - Strong Collaboration: Skilled in leading cross-functional teams, driving collaboration, and ensuring the successful delivery of large-scale solutions. Benefits - Competitive compensation, variable bonus and performance-based reward opportunities, and retirement programs. - Medical, dental, and vision insurance. - Generous, flexible time off, plus paid holidays, wellness days, and a company-wide year-end break. - Paid parental leave (including fully paid leave for eligible ZEOs, subject to local policy). - Learning & development stipend to support ongoing growth. - Opportunities to volunteer and give back, including charitable donation matching where available. - Mental wellbeing resources and support. Base Pay Details $150,500 — $194,800 USD

• Analyze and document existing on-premise middleware interfaces (integration flows, message mappings, adapter configurations, and orchestration processes) for cloud migration readiness • Design, develop, and deploy integration flows on a cloud-based iPaaS (Integration Platform as a Service) environment • Convert legacy message mappings — including graphical, XSLT, and Java-based mappings — to cloud-compatible formats • Migrate adapter configurations (IDoc, SOAP, REST, SFTP, JDBC, RFC, AS2, etc.) from the on-premise middleware layer to the cloud integration platform • Perform unit testing, integration testing, and support UAT for all migrated interfaces • Monitor, operate, and troubleshoot integration flows using cloud operations and observability tooling • Collaborate with functional business teams across Finance, Supply Chain, Sales, and Manufacturing, as well as middleware architects, to ensure business continuity throughout the migration lifecycle • Produce and maintain migration artifacts including mapping specifications, technical design documents, and runbooks

• Join early-stage calls with Strategic Account Executives to lead technical discovery and demos. • Deliver compelling live demos tailored to customer pain points and use cases. • Translate customer pain points into relevant AI-powered solution examples. • Communicate the value and architecture of Unframe clearly and confidently. • Build credibility and trust with prospects while becoming a strategic partner in shaping their AI vision. • Share insights with Product, Marketing, and R&D teams based on real-world conversations.

Role Description We are operating large-scale AI training and inference data centers, and we need an expert who can see the entire stack at once — from the chiller plant and switchgear to the GPU fabric and the Kubernetes scheduler. This role spans facilities/OT telemetry (cooling, power) and IT/AI infrastructure observability (compute, network, accelerators), unified by a single goal: complete, real-time, predictive visibility into how AI infrastructure consumes power, generates heat, moves data, and delivers compute. You will design the observability platform that ingests signals from building and electrical systems, server and network fabrics, Kubernetes, and GPU/accelerator clusters — then apply AI/ML models on top of that telemetry to optimize utilization, predict failures, reduce energy cost, and surface insights operators can act on. You are equally comfortable reading a BACnet point list and a GPU NVLink topology, and you can explain to both facilities and platform teams why their data belongs in the same system. Qualifications - 8+ years in infrastructure, SRE, observability, or data center engineering, with 3+ years in an architect or principal-level role. - Demonstrated experience designing and operating observability platforms at scale (metrics, logs, traces). - Expertise in Datadog, Dynatrace, Grafana, Prometheus and Grafana. - Hands-on experience integrating BMS and EPMS data, and a working understanding of data center mechanical and electrical systems (cooling topologies, power distribution, redundancy, capacity). - Strong systems monitoring background — Linux/server fleets, hardware health, baseboard management (IPMI/Redfish). - Strong network monitoring background, including high-performance / low-latency fabrics relevant to AI workloads. Expertise in SNMP, WMI. - Production experience with Kubernetes and observability of containerized workloads. - Experience operating or monitoring GPU / AI-accelerator clusters and understanding of distributed training/inference behavior. - Practical experience applying AI/ML models to operational data (anomaly detection, forecasting, or AIOps), and comfort using LLMs to derive insights and automate analysis. - Proficiency in at least one language for data/automation work (Python preferred), and infrastructure-as-code practices. Requirements - Define and own the end-to-end observability architecture covering metrics, logs, traces, and events across facilities and IT domains. - Establish standards for instrumentation, telemetry pipelines, data retention, cardinality management, and a unified data model that lets power, thermal, network, and compute signals be correlated in one place. - Design for scale: hundreds of thousands of time series per site, high-frequency power and thermal sampling, and GPU-cluster-level granularity. - Integrate Building Management System (BMS) telemetry — CRAC/CRAH units, chillers, cooling loops, airflow, temperature/humidity, leak detection — into the central observability platform (BACnet, Modbus, MQTT, OPC-UA). - Integrate Electrical Power Monitoring System (EPMS) data — switchgear, UPS, PDUs, busways, branch-circuit metering, generators — for real-time power draw, capacity, and quality monitoring (Modbus, DNP3, IEC 61850). - Build correlated views of power and thermal behavior against compute workload so operators understand cause and effect (e.g., a training job's effect on rack power and inlet temperatures). - Partner with facilities engineering on PUE, capacity planning, stranded-power recovery, and thermal optimization. - Architect observability for AI/GPU clusters — accelerator utilization, memory pressure, thermals, ECC/Xid errors, power capping, and job-level efficiency (e.g., via NVIDIA DCGM, accelerator telemetry exporters). - Instrument Kubernetes environments running AI/ML workloads: cluster, node, pod, and workload metrics, scheduler behavior, GPU/accelerator allocation, and operator health. - Provide visibility into training and inference pipelines — throughput, queue depth, checkpoint behavior, straggler detection, and cost-per-token / cost-per-training-step metrics. - Surface noisy-neighbor, fragmentation, and underutilization patterns across multi-tenant clusters. - Design monitoring for high-performance data center fabrics, including the AI back-end network (RDMA, InfiniBand and/or RoCE Ethernet) and front-end/management networks. - Capture fabric health, congestion, link errors, latency, and bandwidth utilization using streaming telemetry, SNMP, gNMI/gRPC, NetFlow/sFlow, and fabric managers (e.g., InfiniBand UFM). - Correlate network behavior with distributed training performance to diagnose collective-communication bottlenecks. - Apply ML and AI models to the telemetry estate for anomaly detection, predictive maintenance, capacity forecasting, and automated root-cause analysis. - Build models and pipelines that recommend (or automate) actions: dynamic cooling and power optimization, workload placement, power capping under thermal/electrical constraints, and failure pre-emption. - Leverage LLMs and modern AI techniques to summarize incidents, accelerate root-cause investigation, query telemetry in natural language, and generate operator-facing insights from large volumes of logs and metrics. - Establish the feedback loop where observability data trains the models that, in turn, optimize the infrastructure being observed. - Act as the technical authority connecting facilities, network, platform, SRE, and AI/ML teams around a shared observability practice. - Define SLOs, alerting strategy, and on-call signal quality; drive down alert noise and mean-time-to-resolution. - Mentor engineers and set the technical direction for the observability roadmap. Benefits - Experience with tooling such as OpenTelemetry, VictoriaMetrics/Thanos, Loki, Tempo, Elastic, Splunk. - Familiarity with OT/industrial protocols: BACnet, Modbus, OPC-UA, DNP3, IEC 61850, MQTT. - Familiarity with GPU/accelerator telemetry (NVIDIA DCGM and exporters) and InfiniBand/RDMA monitoring (e.g., UFM). - Experience with network telemetry: gNMI/OpenConfig streaming, SNMP, NetFlow/sFlow. - Experience with time-series data at high cardinality, stream processing, and data lake/warehouse patterns for telemetry. - Background in MLOps, model deployment, or building data/feature pipelines for operational ML. - Exposure to power and cooling optimization, PUE improvement, or sustainability/energy-efficiency initiatives. - Relevant certifications (e.g., data center facilities, Kubernetes/CKA, cloud or networking) are a plus.