Backblaze

• Manage day-to-day operational health across a portfolio of strategic accounts with multi-million dollar ARR — you're the person the customer calls when something needs to get done • In addition to the day to day operational health, you will own the commercial relationship between executive touchpoints — you are the account's day-to-day voice of Backblaze • Drive expansion pipeline through relationship depth, not just product knowledge — you find the next opportunity because people tell you things • Negotiate renewal terms and expansion pricing in partnership with executives, bringing account context and relationship capital to the table • Hit defined ramp targets in your first 90/180/270 days, establishing yourself as a trusted presence in each account and building expansion pipeline quickly • Keep accounts running smoothly — coordinating across Support, Engineering, and Product to resolve issues, track open items, and ensure commitments are met • Farm accounts actively and systematically — always listening for new workloads, infrastructure challenges, or organizational changes that signal expansion opportunity • Build and maintain working relationships across multiple stakeholder layers — Infrastructure, DevOps, Storage Engineering, FinOps, and Procurement — while Backblaze executives own the C-suite relationship • Partner with Solutions Engineers on technical discovery, architecture reviews, and POC engagements when expansion opportunities surface • Translate deep technical product capabilities — B2 Cloud Storage, S3-compatible APIs, multi-cloud egress optimization, data durability architectures — into solutions that map to real customer problems • Prepare executives for high-stakes customer meetings with account context, relationship intelligence, and clear briefings • Maintain accurate pipeline, account health, and forecast data in CRM — your accounts should never be a black box to the team

• Partner closely with the Talent Acquisition team to support full-cycle recruiting for open roles across Backblaze, both in the US and internationally • Own the recruiting process end to end — from req kickoff through offer acceptance and hire • Source, screen, and move candidates through the pipeline efficiently and with a strong candidate experience • Build and maintain relationships with hiring managers to understand role requirements and team needs • Support recruiting across both technical and non-technical functions

• Build and execute Backblaze's partner ecosystem strategy across OEMs, cloud infrastructure providers, ISVs, distributors, and marketplaces. • Develop strategic partnerships with GPU cloud providers, AI infrastructure companies, and compute platforms to position Backblaze as the preferred storage solution. • Build and scale MSP, CDN, NeoCloud, and xSP partner programs, including program structure, partner enablement, compensation models, and rules of engagement. • Expand relationships with backup, data management, and data platform ISVs through technical certifications, reference architectures, and co-sell programs. • Develop Backblaze's marketplace strategy across AWS Marketplace, Azure Marketplace, and emerging AI marketplaces. • Design and launch a scalable partner program, including partner tiers, deal registration, enablement, governance, and incentive structures. • Partner closely with Sales, Product, Marketing, and Executive Leadership to create repeatable partner-led revenue motions. • Build reporting and metrics that measure partner performance, pipeline contribution, and program effectiveness.

• Lead and develop the team. Manage, coach, and grow a team of 7–8 Technical Account Managers. Invest in their development, raise the performance bar, and build a team culture where exceptional work is the norm. • Own enterprise customer outcomes. Partner with Sales, Customer Success, and Product to ensure our most strategic customers are receiving a consistently excellent, proactive support experience that reflects the Backblaze standard. • Scale the operating model. As our enterprise customer base grows, build the delivery frameworks, workflows, and service standards that allow the team to scale with it — without losing what makes us great. • Drive operational performance. Establish the KPIs, reporting cadences, and feedback loops that give leadership clear visibility into how enterprise support is performing — and where to invest next. • Build for scale. Identify the tooling, automation, and process improvements that increase capacity and consistency as we grow — so headcount isn't the only answer. • Be the connective tissue. Work cross-functionally with Engineering, Product, and Sales to resolve escalations, influence roadmap priorities, and ensure the enterprise customer voice is represented internally. • Codify and elevate. Partner with the Principal TAM and Senior Director to turn the team's deep expertise into repeatable, trainable processes that raise the floor across the entire function.

• Own the post-sales experience by defining service standards, escalation protocols, and onboarding playbooks • Build the Voice of Customer engine by synthesizing feedback into clear insights for improvements • Create a scalable enablement program that accelerates customer success • Define the end-to-end experience for white-label partners and enterprise accounts • Track program health using indicators to improve operations • Flag feature gaps and service readiness risks, writing playbooks and runbooks for consistency

• Available to Lead and govern the end-to-end incident management lifecycle, including detection, triage, escalation, and resolution. • Drive major incident management (MIM) processes and communications. • Improve MTTR (Mean Time to Resolution) through automation and process optimization. • Establish and maintain incident response playbooks and runbooks. • Maintain and improve intelligent heatmaps leveraging AI/ML to identify recurring technical themes and prioritize long-term remediation. • Implement trend analysis and proactive problem identification using observability data and AI. • Track and manage problem records to closure. • Govern change management processes (lead the CAB), ensuring safe, compliant, and low-risk deployments. • Define and enforce change policies, risk assessments, and approval workflows. • Drive continuous improvement in release and deployment practices. • Maintain a strong understanding of system architecture and monitoring strategies, identifying gaps and opportunities for improvement. • Partner with engineering teams to improve system resilience and performance. • Reduce alert fatigue by improving signal-to-noise ratio in monitoring systems. • Leverage AI/ML for anomaly detection, predictive alerting, and automated root cause analysis. • Implement AI-driven solutions to optimize incident response and operational workflows. • Analyze large-scale operational data to identify patterns and recommend improvements.

• Take scoped use cases from the Director, AI Enablement and build working solutions: prompt layers, automation flows, lightweight agents, RAG implementations, and API integrations that connect AI tools to the systems business teams already use • Build working prototypes quickly - the goal is to show a team what is possible in days, not weeks, then refine based on what actually lands • Translate a documented workflow assessment into a technical design, select the right tools for the job, and own the build through to a deployable solution • Maintain solution documentation that is clear enough for handoff, iteration, and audit - not just for engineers, but for the business owner who will live with it • Spend time inside the functions you are building for - Finance, HR, Legal, Marketing Ops, Customer Success, and others - understanding how the work actually flows before you redesign it • Run working sessions with team members to map current-state workflows, surface friction points, and identify where AI creates real leverage versus where it creates noise • Build credibility with non-technical stakeholders by showing them working solutions, not decks - and by being honest about what will and will not work • Support user acceptance testing and early adoption, troubleshooting issues close to launch and iterating based on real usage • Build demos and proof-of-concepts that help the Director, AI Enablement make the case for a use case - or disqualify it early before resources are committed • Contribute to vendor and tool evaluations with a builder's perspective: not just whether a tool claims to do something, but whether it actually works in the context of a specific workflow • Feed learnings from the build process back into the program - what worked, what did not, what should be standardized across functions • Work on tactical quick wins and longer-horizon strategic builds at the same time, switching contexts across active use cases as priorities shift

• Develop and maintain short, medium, and long-term capacity demand and hardware deployment forecasts across storage, compute, and network domains within the platform • Build predictive models that translate business demand signals into infrastructure requirements using historical utilization, growth trends, product sales plans, hardware lifecycle roadmaps, and other key business inputs • Partner with Infrastructure, Production, and Network Engineering teams to align capacity plans with system design and scaling initiatives • Develop and automate forecasting pipelines, simulation calculators and tools, and capacity dashboards to improve data quality, reduce manual analysis, and provide stakeholders clear visibility into platform usage and cluster health metrics • Monitor and analyze cluster and system-level utilization and performance across CPU, memory, IOPS, and network resources • Adjust deployment plans and recommended configurations in real-time to maintain adequate headroom and system stability in support of delivering a world-class customer experience • Partner with service and platform owners to develop headroom and live buffer policies, optimize hardware BoMs, leverage virtualized orchestration, and reduce product cost • Work in lockstep with Operations and Finance peers to align capacity plans and hardware requirements with capital budgets, cost targets, and financial outcomes • Support strategic optimization initiatives across infrastructure investments, engineering development, and operations processes, contributing to long-term infrastructure strategy and capital planning • Lead efforts to evaluate, procure, and provision requests for new or additional hardware, working with Systems and Network Engineering, SRE, NOC, and Data Center Operations teams to identify and deliver optimal solutions • Maintain alignment with Product and Sales to support customer onboarding, growth, and demand variability • Communicate complex capacity and infrastructure insights clearly to technical and non-technical stakeholders

• Implement and maintain open source AI integrations, frameworks and example applications using languages commonly used in the open source ecosystem, including Python, TypeScript, Go, and Rust. • Build and maintain public sample applications, SDK extensions, notebooks, and reference implementations demonstrating AI and cloud storage best practices. • Use AI-assisted development tools, including Claude Code, to accelerate implementation while validating correctness, security, and performance. • Contribute to open source projects through code contributions, issue triage, reviews, and ongoing maintenance. • Ensure all open source assets meet high standards for testing, documentation, reproducibility, and developer usability. • Maintain clean, well-structured GitHub repositories intended for external adoption and community contribution. • Collaborate with Product and QA to ensure integrations reflect real customer AI workflows and Backblaze B2 usage patterns.

• Own the use case pipeline. Triage incoming requests, refine problem statements, size value, and maintain a prioritized queue across functions. Lead each use case through discovery, scoping, pilot, and launch - coordinating functional owners, IT, Engineering, Legal, and Finance as required. • Do the workflow work. Conduct structured current-state assessments with department teams. Map how work actually happens today. Find where volume, repetition, or research intensity make AI a strong fit - and surface the use cases that would never appear in a top-down scan. • Design the right solution. Evaluate whether a given workflow calls for an off-the-shelf tool, a prompt layer, a vendor product, or a custom build. Engage Engineering and IT when integration or custom development is required. Document baselines before launch. Measure the delta after. • Govern the tool stack. Own the AI vendor evaluation process end-to-end: fit assessment, security and privacy review, pilots, and build-vs-buy recommendations. Maintain the AI Tool Registry with zero ungoverned deployments. Track spend per team monthly with cost-per-outcome visibility available to the executive team. • Keep leadership informed. Maintain a live view of every active use case, its status, its owner, and its impact. Produce outcomes reports. Surface role and change implications early - before they become problems.