Qumulo's cloud data platform manages exabytes of the world's most demanding data, unifying files, objects, and every workload across edge, core, and cloud. Over 1,100 customers run it across media, healthcare, energy, research, and the public sector. Qumulo opened its European software R&D hub in Cork in March 2026, and this role is part of building that team out.

About the Position

You'll own the pipeline that gets Qumulo paid. Our consumption metering system ingests customer usage every hour, rates it, and submits it to the AWS, Azure, and Google Cloud marketplaces. It works today. It was built to a solid first version, and it now needs a named owner: someone to run it in production, and harden it to enterprise grade.

This is one engineer and one revenue-critical system, with the authority to decide how it runs. You inherit the codebase and its documentation, then set the reliability bar yourself. You'll be a part of the Engineering team and work with Sales Operations, Finance, and Customer Success across Cork and Seattle, and with the product team. Building what comes next in collaboration with these teams to develop insights and features as part of customer facing solutions. Agentic developer tools are part of how this team works, for ramping and for production, with the engineering judgment to check what they produce.

What you'll own

Take over the consumption metering ingest and marketplace submission pipelines: Python on AWS serverless (Lambda, EventBridge, SNS/SQS), an Azure Functions component, the m3ter platform, and the AWS, Azure, and Google Cloud marketplace APIs.
Run the system as production from day one, including code review, CI/CD, deployment, and incident response, including the on call rotation.
Build the alerting, retries, idempotency, and self-recovery that let a missed submission resolve itself before anyone gets paged.
Write the runbooks that let on-call teams act safely without you.
Replace bespoke per-product submission logic with one submission engine, and rebuild the parts of the inherited design that are structurally wrong.
Act as the named risk steward for revenue correctness. Surface operational and architectural risks early, propose mitigations, own them to resolution.
Partner with Product to turn metering data into something customers use: live balances, usage accurate to the hour, burn-rate forecasting.
Translate between Finance, Sales Operations, Customer Success, and the codebase. Find the requirement under an ambiguous ask, and say where logic belongs when it doesn't belong in the pipeline.
Lead the technical relationship with our external billing SaaS partners.

What we're looking for

You've been the person responsible for a production system that mattered. You owned an incident end to end and changed something because of it.
You can talk concretely about idempotency, retries, and alerting, from having needed them.
You have four or more years of professional software engineering experience.
You write Python or can prove a fast ramp. The inherited codebase is Python-first, though we'll take a strong engineer with a track record of picking up an unfamiliar stack fast.
You've shipped serverless, event-driven systems on at least one major cloud (Lambda, EventBridge and SQS, Azure Functions, or the Google Cloud equivalents), and you can map the patterns between them.
You've integrated with external systems in production: billing platforms, cloud marketplaces, payment providers, ERP or CRM.
You pull yourself toward unfamiliar problems, and you know the edges of what you know.
You explain a technical constraint to someone who doesn't share your background, and hold the line without turning it into a fight.
You work with modern CI/CD, Git-based workflows, and infrastructure as code.
Bonus if you've used m3ter, Stripe Billing, Metronome, Orb, or a similar metering platform.

Further Details:

Competitive salary and benefits package, including equity, pension, and healthcare
Flexible working arrangements, with 3 days onsite and 2 remote
Reimbursement for home phone and internet

About Qumulo:
Built for the most demanding enterprise workloads, from AI and HPC simulations to Splunk Observability, genomics and PACS medical imaging, geospatial datasets, media editing and rendering, and video surveillance, Qumulo unlocks the full power of an organization’s information. Our platform unifies file and object storage across data centers, edge, and public clouds, enabling efficient, accelerated computing and extending the reach of data unbound by protocol or transport limitations.

With more than 1,100 customers and exabytes of data under management, Qumulo powers mission-critical workloads anywhere real-time access to massive file datasets is non-negotiable. Qumulo delivers radical simplicity, hardware freedom, exceptional customer support, and a true hybrid-cloud architecture.

Our Values:
At Qumulo, we are building an open and collaborative culture where people can do their best work with customers as our magnetic field. We act as owners, we share by default, we are data driven and experimental and as an inclusive workplace, we encourage and celebrate multiple points of view. As part of our culture we believe diversity drives innovation.

Equal Opportunity Employer:
Qumulo is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, disability, military status, national origin, or any other characteristic protected under federal, state, or applicable local law. For more information on Qumulo's Applicant Privacy Policy, please visit: https://qumulo.com/applicant-employee-privacy-notice

About the company

Qumulo provides a software platform to store, manage, and curate unstructured data at exabyte scale across edge, core, and cloud environments. The product is used on a subscription basis, deployed in multiple locations, and often complemented by professional services to help implement and optimize the solution. It differentiates itself by handling massive unstructured data across diverse environments in a single system and by earning Gartner recognition and a high customer satisfaction score. The goal is to help industries like healthcare, media, research, and government store, access, and curate large volumes of data generated by IoT, surveillance, and high‑resolution content.

Company Size

501-1,000

Company Stage

Series E

Total Funding

$347.3M

Headquarters

Seattle, Washington

Founded

2012

Get referred to Qumulo

See people who can refer or advise you

Simplify's Take

What believers are saying

July 2026 Databricks OpenSharing expands Qumulo into AI and analytics workflows.
May 2026 NeuralProtect adds ransomware defense, strengthening security-driven upsell potential.
IBC 2026 Colorfront collaboration turns Qumulo into searchable post-production infrastructure.

What critics are saying

Qumulo cut 80 jobs in June 2026, signaling margin pressure and reorganization.
HPE stopped reselling Qumulo in March 2026, weakening channel reach and hardware-led deals.
Hyperscalers and Databricks commoditize storage control; Qumulo becomes a feature, not a company.

What makes Qumulo unique

NeuralSearch indexes file metadata inside storage, not through external crawlers.
Qumulo spans on-prem, edge, and cloud with one distributed file platform.
Its 2026 partnerships with Databricks, Microsoft, and Cisco deepen enterprise workflow lock-in.

Help us improve and share your feedback! Did you find this helpful?

Benefits

Healthcare plan paid 100% for employees

Flexible/unlimited paid time off

Transportation subsidy for office commuters

Cell phone service reimbursement

401(k) and Roth retirement planning with Fidelity

Stock option grants offered to all full-time employees

Parental leave (20 weeks for birthing mothers, 8 weeks for adoption or non-birthing parents)

Growth & Insights and Company News

Headcount

6 month growth

↑ 0%

1 year growth

↑ 0%

2 year growth

↓ -4%

Provideocoalition

Sep 11th, 2026

Colorfront and Qumulo at IBC2026: metadata does not have to die in transit.

Colorfront and Qumulo at IBC2026: metadata does not have to die in transit. Support ProVideo Coalition Filmtools. Filmmakers go-to destination for pre-production, production & post production equipment! Colorfront and Qumulo team-up to make every frame of footage searchable in plain English for the life of a production. September 11, 2026 The two companies will be at IBC 2026, to show their solution to solve one of post-production's most persistent and expensive problems: lost metadata. Qumulo and Colorfront, the Academy and Emmy Award-winning developer of Transkoder and On-Set Dailies, announced a strategic collaboration to solve one of post-production's most persistent and expensive problems: data associated with shot details and specifications created on-set almost never survive the journey to the people who need it. The two companies will preview their joint work during IBC 2026, 11-14 September, Amsterdam. The new joint work writes on-set metadata directly into the file, so lens, colour and framing decisions survive every copy, move and archive tier, and stay searchable in plain English years later. The aim is to solve the problem faced by productions until now:metadata dies in transit. In fact, every frame shot on a modern production arrives with a substantial body of information attached, known as metadata in the technology community. Metadata includes camera make and model, sensor mode, resolution and frame rate. Beyond that, systems also capture lens, focal length, aperture, focus distance, timecode, reel and scene identifiers, colour intent as ASC CDL values and LUTs, framing intent, sound roll and sync points, script supervisor notes, circle takes, VFX flags... the list is ever growing and evolving with new technologies. There only one problem: almost none of it reaches the people downstream, until now. The information is fragmented across camera-original headers, sidecar files, ALEs, EDLs, spreadsheets and a colourist's local project database. Each hand-off (set to dailies, dailies to editorial, editorial to VFX, VFX to finishing) is an opportunity for the loss of important metadata, flattened into a filename, or transcribed by hand. By the time an assistant editor asks for "the handheld night exteriors on the 32mm, the ones the DP circled," the answer requires scrubbing through hours of material. Put simply, losing the metadata results in production delay. The costs are difficult to calculate, but imagine 20 artists working on a project overnight, and each takes between 15-20 minutes of an assistant's time whenever they need a frame and this happens on average two times per artist per hour. That means somewhere between 10 and 13 hours per night are wasted. Over the course of a year, across multiple production pipelines, a studio may bleed millions in this inefficiency alone, and that doesn't factor in the rework required if the assistant pulls the wrong files. The solution is simple: write the metadata to the file, then make the file searchable. The collaboration joins two pieces that each solve half the problem. Colorfront Transkoder and On-Set Dailies already sit at the point in the pipeline where metadata is richest. They ingest camera-original material from every major manufacturer and reads what the camera recorded. Colorfront then adds considerably more during processing: colour transforms and ACES pipelines applied, ASC CDL and LUT decisions, HDR and Dolby Vision metadata, framing decisions, audio sync, and (in Transkoder/On-Set Dailies 2026) AI-derived information including text detection and automated quality-control findings such as clipped highlights, crushed blacks and judder. Previously, the expanded metadata was written into deliverables and sidecars, and its usefulness has decayed from that point forward. Today, Qumulo NeuralSearch is storage-native search built into the Qumulo Data Platform. It indexes file metadata continuously as data lands, with no external crawler and no separate search infrastructure, and answers SQL, natural-language and semantic queries directly against the filesystem. In collaboration, Colorfront and Qumulo join forces at the file-layer itself. Rather than emitting metadata to a sidecar that can be separated from the picture, Transkoder writes what it discovers and derives into Qumulo user metadata attached to the file object, through the Qumulo Core REST API. The Qumulo indexer lifts those values into typed, queryable columns as part of its normal indexing pass. Using Colorfront + Qumulo, metadata is packaged as a property of the media, carried by the filesystem, surviving every copy, move, and tier transition, and searchable from the moment it is written. Immediately saving thousands of hours of work every night across the overnight ecosystem. Here is more information about the solution: What it looks like in practice Once the metadata is resident on the file and indexed, retrieval stops being a browsing problem and becomes a query. - Instant dailies. "Everything from B-camera on day 14 with the DP's circle-take flag" resolves to a selection in seconds rather than an evening of assembly. - VFX pulls. "All shots on the 32mm with the green-screen flag, at 4K or above, that have not been pulled yet" returns not just the plates but the colour transforms and framing metadata the vendor needs alongside them: the pull arrives complete rather than as a list of filenames a coordinator then has to chase. - Conform and finish. Shots are located by the colour and framing decisions recorded against them, rather than by reconstructing intent from an EDL and a folder structure. - Archive and reuse. Material from a production that wrapped two years ago is as findable as material shot this morning, because the searchable information is attached to the frames rather than to a project file that has since been archived, corrupted or superseded. At IBC, Qumulo and Colorfront will preview the workflow end-to-end: metadata originating on-set, expanded by On-Set Dailies and Transkoder during processing, written to the file on Qumulo, and retrieved through NeuralSearch's natural-language interface, including inside Adobe Premiere Pro, through a NeuralSearch panel that lets an editor find and load a shot without leaving the timeline. "Storage has spent thirty years getting better at holding frames and almost no time getting better at helping anyone find them," said Douglas Gourlay, Chief Executive Officer, Qumulo. "Colorfront sits where the richest metadata in the entire pipeline is created and expanded. Making that information a durable property of the file, indexed and searchable from the moment it lands, changes what a storage platform is for. The picture stops being an opaque object you have to open to understand." "Transkoder has always known a great deal about every frame it touches, what the camera recorded, what the colourist decided, how the shot is meant to be framed and graded," said Mark Jaszberenyi, Chief Executive Officer, Colorfront. "Until now most of that knowledge stopped being useful the moment the render finished. Writing it to the file on Qumulo, where it can be searched in plain language, means the work our software does on-set keeps paying off through editorial, VFX and finishing, and long into the archive." The joint workflow is in active development and will be shown as a preview at IBC 2026. Colorfront Transkoder/On-Set Dailies 2026 is available now from Colorfront. Qumulo is hosting demonstrations and meetings throughout IBC aboard the Para Todos, a restored 1924 Amsterdam tugboat docked for the week.

GetLatka

Aug 26th, 2026

Chronosphere vs Qumulo: Revenue, Funding & Team size compared.

Chronosphere vs Qumulo: Revenue, Funding & Team size compared. Chronosphere generates $160M in revenue; Qumulo generates $115.7M. Chronosphere is 1.4x bigger than Qumulo by revenue. The table below compares Chronosphere and Qumulo on funding, valuation, customers, team size and headquarters - every figure GetLatka has verified for each company. | Company | ChronosphereThis company | Qumulo | | Revenue | $160M | $115.7M | | Valuation | $3.4B | $1.2B | | Funding raised | $254.4M | $345.5M | | Team size | 307 | 569 | | Growth | 278.1% | Not disclosed | | Founded | 2019 | 2012 | | HQ | New York, United States | Seattle, United States | Want the full dataset? GetLatka tracks revenue, funding and team history for thousands of SaaS companies, with charts, growth rates and founder interviews. Chronosphere at a glance. Chronosphere generates $160M in revenue with 307 employees, headquartered in New York, United States. * Revenue - $160M * Valuation - $3.4B * Funding - $254.4M * Team size - 307 * Founded - 2019 Chronosphere is a cloud-native monitoring and observability platform that helps businesses manage and analyze their complex infrastructure and application environments. The platform provides real-time metrics, logs, and tracing data to... Qumulo at a glance. Qumulo generates $115.7M in revenue with 569 employees, headquartered in Seattle, United States. * Revenue - $115.7M * Valuation - $1.2B * Funding - $345.5M * Team size - 569 * Founded - 2012 Modern enterprises have to manage exponentially-growing exabyte-scale data stores comprised mostly of unstructured data. Someone (often IT) has the difficult job of staying on top of managing these data stores, which becomes more difficult... Other Chronosphere alternatives. Chronosphere competes with more than the companies on this page. Browse the full alternative lists to compare revenue, funding and team size across the category. Chronosphere vs Qumulo: frequently asked questions. Is Chronosphere or Qumulo bigger? Chronosphere is the bigger company by revenue, at $160M against $115.7M for Qumulo. How much revenue does Chronosphere make? Chronosphere generates $160M in annual revenue with a team of 307. How much revenue does Qumulo make? Qumulo generates $115.7M in annual revenue with a team of 569. How much funding has Chronosphere raised? Chronosphere has raised $254.4M in total funding since it was founded in 2019. How much funding has Qumulo raised? Qumulo has raised $345.5M in total funding since it was founded in 2012.

Storage Newsletter

Jul 24th, 2026

Qumulo announces NeuralSearch and ISV partnership with Databricks.

Qumulo announces NeuralSearch and ISV partnership with Databricks. Qumulo partners with Databricks to unify governed access to data for AI and analytics by integrating OpenSharing with Qumulo NeuralSearch. Qumulo, announced a partnership with Databricks, integrating Qumulo NeuralSearch with Databricks OpenSharing, the first open protocol to cover agent skills, AI models, and unstructured data.Customers can discover, query, and collaborate on tabular data across core, cloud, and edge environments, without replicating the data or adding operational complexity. The result: a single, geo-distributed view of enterprise data via Unity Catalog that spans locations, regions, and cloud providers. "By combining Qumulo NeuralSearch with [Databricks] OpenSharing, we're enabling customers to securely discover, query, and collaborate on data across data centers, edge locations, and public clouds, in real time, without moving the data itself." NeuralSearch is Qumulo's storage-native search platform. It turns unstructured data into searchable intelligence using SQL, natural language, and semantic search with no crawlers or third-party indexing required. NeuralSearch is built into Qumulo's Data Platform, including Cloud Data Fabric. With OpenSharing and Qumulo, users can discover and access datasets across on-premises, remote locations, and cloud regions and providers without replicating data. For Chief Data Officers and VPs of Data Analytics, governing massive unstructured data sets while accelerating time-to-value for AI is a critical challenge. This integration empowers organizations to fuel Generative AI, Retrieval-Augmented gen (RAG) applications, and advanced analytics. By securely connecting Databricks compute directly to Qumulo's edge-to-core storage framework, data teams can drastically reduce the latency traditionally associated with geo-distributed workloads, accelerating time-to-insights without the burden of constant data replication. "Organizations can no longer afford the cost, complexity, and delays of copying massive datasets across environments just to support AI and analytics," said Brandon Whitelaw, SVP and head, product, Qumulo. "By combining Qumulo NeuralSearch with OpenSharing, we're enabling customers to securely discover, query, and collaborate on data across data centers, edge locations, and public clouds, in real time, without moving the data itself. Together, we're helping organizations accelerate AI initiatives, simplify governance, and unlock faster insights from distributed data, all while maintaining a single source of truth." As enterprises scale agentic AI, AI applications, and advanced analytics, open access to governed data across distributed environments is becoming increasingly critical. By combining Qumulo's distributed data capabilities with the Databricks Data + AI Platform, customers can build and scale AI workloads on trusted, accessible data without creating additional silos. Marked by successful market and technical validation of the Qumulo Data Platform, the Databricks partnership allows Qumulo to support a massive market by offering an open architecture that avoids vendor lock-in for customers in verticals such as financial services, life sciences, and manufacturing. The collaboration enables seamless data flows between storage and advanced AI analytics tools across all major cloud providers. The key strategic advantages for joint customers include: * Open access to data across the enterprise: Qumulo leverages distinct technical advantages over rivals, notably its robust multi-format support. It seamlessly handles open table formats like Apache Iceberg and Delta Lake, alongside their underlying file formats like Parquet, simultaneously. It also serves as a unified file and object platform, being the first to seamlessly project data across multi-region and multi-cloud environments * Unified Governance for AI: By integrating with OpenSharing, Qumulo brings globally distributed data under Databricks Unity Catalog, ensuring centralized governance, security, and compliance across the entire data lifecycle * Workflow Acceleration: By allowing data to flow smoothly from Qumulo through Databricks for transformation, the partnership accelerates the "time-to-data-product" and reduces data cleansing overhead "Customers consistently tell us they want the freedom to leverage their enterprise data across a broad ecosystem without being constrained by proprietary architectures," said Stephen Orban, SVP, product ecosystem & partnerships, Databricks. "Our partnership with Qumulo reflects the commitment from Databricks to open platforms by enabling a non-proprietary, single source of truth for enterprise data, giving customers the architectural flexibility to seamlessly use their data across tools and environments." Read also: Powered by open source OpenSharing, our new storage partner ecosystem brings Databricks Data Intelligence Platform directly to your on-premises and hybrid infrastructure - without copying a single byte Top News By Philippe Nicolas June 24, 2026 | News A new open standard for sharing of data and AI assets across platforms and organizations Enabling enterprises to seamlessly extend file workloads into the cloud instantly, avoiding multi-month hardware lead times without disruptive migration or application refactoring And increases GPU utilization with a 90%+ more effective, practical approach by eliminating staging delays, idle GPU costs, and the data-gravity constraints that slow AI In honor of National Cancer Research Month, Qumulo spotlights research into cancer treatment breakthroughs

Blocks & Files

Jul 17th, 2026

Qumulo pals up with Databricks.

Qumulo pals up with Databricks. Chris Mellor STORAGE EDITOR Blocks & Files editor Published fri 17 Jul 2026 // 18:31 UTC Qumulo has integrated its NeuralSearch capability with Databricks OpenSharing so that Databricks users and agents get access to Qumulo's stores when searching the Databrick's repository. Databricks launched OpenSharing in June, saying it was an open, vendor-neutral protocol for sharing AI assets, including Agent Skills, AI models, and unstructured data, across organisations and platforms. It builds on Databricks' Delta Sharing, by adding support for Iceberg IRC (Iceberg REST Catalog) clients, expanding data providers' reach to more recipients. Customers can access on-premises or private-cloud assets via OpenSharing, including through storage partners such as Everpure, MinIO, Qumulo and VAST Data. Qumulo has now launched its contribution to OpenSharing. NeuralSearch is its storage-native search platform, turning unstructured data into searchable intelligence using SQL, natural language, and semantic search with no crawlers or third-party indexing required. NeuralSearch is built into Qumulo's Data Platform, including Cloud Data Fabric. With OpenSharing and Qumulo, customers get a single, geo-distributed view of enterprise data via Unity Catalog that spans locations, regions, and cloud providers. They can can discover, query, and collaborate on tabular data. Brandon Whitelaw, SVP and Head of Product at Qumulo, said: "Organizations can no longer afford the cost, complexity, and delays of copying massive datasets across environments just to support AI and analytics. By combining Qumulo NeuralSearch with OpenSharing, we're enabling customers to securely discover, query, and collaborate on data across data centers, edge locations, and public clouds, in real time, without moving the data itself. Together, we're helping organizations accelerate AI initiatives, simplify governance, and unlock faster insights from distributed data, all while maintaining a single source of truth." The two companies say that, by securely connecting Databricks compute directly to Qumulo's edge-to-core storage framework, data teams can drastically reduce the latency traditionally associated with geo-distributed workloads, due to constant data replication. Qumulo says the Databricks partnership allows it to support a massive market by offering an open architecture that avoids vendor lock-in for customers in verticals such as financial services, life sciences, and manufacturing. The collaboration enables seamless data flows between storage and AI analytics tools across all major cloud providers. The key strategic advantages for joint customers include: * Open access to data across the enterprise: Qumulo claims technical advantages over rivals, such as its robust multi-format support for open table formats like Apache Iceberg and Delta Lake, alongside underlying file formats like Parquet. It serves as a unified file and object platform, saying it's "the first to seamlessly project data across multi-region and multi-cloud environments." * Unified Governance for AI: Qumulo brings globally distributed data under Databricks Unity Catalog, ensuring centralized governance, security, and compliance across the entire data lifecycle. * Workflow Acceleration: By allowing data to flow smoothly from Qumulo through Databricks, the partnership accelerates the "time-to-data-product" and reduces data cleansing overhead. Stephen Orban, SVP, Product Ecosystem and Partnerships at Databricks, said: "Customers consistently tell us they want the freedom to leverage their enterprise data across a broad ecosystem without being constrained by proprietary architectures. Our partnership with Qumulo reflects the commitment from Databricks to open platforms by enabling a non-proprietary, single source of truth for enterprise data, giving customers the architectural flexibility to seamlessly use their data across tools and environments." The two companies say that, as enterprises scale agentic AI, AI applications, and advanced analytics, open access to governed data across distributed environments is becoming increasingly critical. That's why Databricks instituted its OpenSharing facility and Qumulo is one of Databricks' storage partner joining in. MinIO is already there, a blog saying its "AIStor Table Sharing builds the open OpenSharing protocol directly into the data platform." Orban said: "By natively integrating OpenSharing, MinIO enables enterprises to securely connect on-premises data to the Databricks Data Intelligence Platform without complex replication, accelerating time-to-insight for hybrid workloads." Blocks & Files understand that Everpure's OpenSharing integration is in private preview as is VAST Data's integration. Databricks has also secured commitments from Cohesity, Commvault, HPE, NetApp, Nutanix, Rubrik, and others to build native integrations by the end of 2026 Bootnote. Databricks Delta Sharing is a zero-copy way of sharing third-party data with customers using its own data. Unity Catalog is Databricks' central metadata management facility.

Associated Press

Jul 16th, 2026

Qumulo partners with Databricks to enable real-time AI data access across distributed environments

Qumulo has partnered with Databricks to integrate its NeuralSearch platform with Databricks OpenSharing protocol. The collaboration enables customers to discover, query, and collaborate on data across core, cloud, and edge environments without replicating it. NeuralSearch is Qumulo's storage-native search platform that transforms unstructured data into searchable intelligence using SQL, natural language, and semantic search. The integration allows users to access datasets across on-premises locations, remote sites, and cloud regions without data replication. The partnership addresses challenges for Chief Data Officers managing massive unstructured datasets whilst accelerating AI time-to-value. It supports Generative AI, Retrieval-Augmented Generation applications, and advanced analytics by connecting Databricks compute directly to Qumulo's edge-to-core storage framework. The collaboration provides unified governance through Databricks Unity Catalog and supports open table formats including Apache Iceberg and Delta Lake.

Qumulo

Member of Technical Staff - Business Systems

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