Cloud Operations Engineer
About the job
- 5+ years of cloud infrastructure administrator experience is required.
- Bachelor's or equivalent work experience degree in Computer Science or Computing Systems equivalent field.
- Background in math will be a plus.
- Experience working with Linux operating systems such as RedHat / CentOS / Amazon Linux is required.
- Significant prior experience with at least one of the Big 3 Cloud providers (AWS/GCP/Azure) is required.
- Must have command-level fluency in Linux environments.
- Familiarity with Linux / Windows diagnostics and tuning will be a plus.
- Must be proficient in the use of Microsoft Office Applications (Outlook / Word / Excel).
- Participate in the On-Call rotation, as a member of the Cloud Managed Services team, and respond to incidents that impact Neo4j cloud infrastructure availability to provide the necessary support for customer cases.
- Work as a member of the Cloud Managed Services Team during business hours, with occasional off-hours scheduled work, to support and manage the Neo4j database infrastructure of the customers and handle and resolve escalations related to cloud hosted environments from customers and/or internal teams.
- Improve, document, and automate operational processes (such as deployments and upgrades) to make them as efficient as possible.
- Gather and analyze metrics from operating systems as well as Neo4j databases to assist in performance tuning and incident diagnostics.
- Document the incidents, procedures, and outcomes of customer communications. Assist in building the Cloud Managed Services knowledge base.
- Install and upgrade Neo4j infrastructure (servers, storage, databases) for our Cloud Managed Service customers.
- Troubleshoot cluster issues across multiple servers, data centers and regions, in a variety of clouds (AWS, Azure, Google Cloud), virtual, and bare metal environments.
- Collaborate and communicate effectively within Customers, Customer Success, Product Engineering, and other Neo4j teams to understand and resolve customer issues completely and expediently.
- Proactive approach to identifying problems, performance bottlenecks, and areas for improvement is necessary to succeed in this position.
- Experience with Ansible, Terraform, and Kubernetes is desirable.
- Experience working with Nagios and Prometheus is desirable.
- Neo4j Certified Professional certification is desirable.
About the company
Neo4j provides a graph database management system that stores data as nodes and relationships to help organizations analyze highly connected data. Its core product, the Neo4j Graph Database, is ACID-compliant and uses Cypher to query and traverse the graph, with additional offerings like AuraDB (cloud hosting), the Graph Data Science library, and Bloom for visualization. Unlike traditional relational databases, Neo4j is built for fast graph traversals and analytics, and it supports a freemium model with paid enterprise options used by many large organizations. The goal is to turn complex connected data into actionable insights through scalable graph storage, advanced analytics, and cloud deployment, positioning the company for broader adoption and potential IPO readiness.
Company Size
1,001-5,000
Company Stage
Grant
Total Funding
$633M
Headquarters
San Mateo, California
Founded
2007
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Simplify's Take
What believers are saying
- Neo4j crossed a 2026 $200 million ARR milestone and keeps hiring.
- Financial Crime Intelligence launched 2026-09-16 for banks and insurers immediately.
- GraphAcademy refresh, MCP server, and 2026 partner growth widen developer and channel adoption.
What critics are saying
- AWS, Oracle, TigerGraph, and Memgraph sell competing graph and fraud tools today.
- Palantir Gotham pressures Neo4j in government deals after the 2026-06-03 GraphAware acquisition.
- If graphs become an AI feature inside clouds, Neo4j loses platform pricing power.
What makes Neo4j unique
- Neo4j models data as relationships, not tables, for multi-hop reasoning.
- GraphAware acquisition on 2026-06-03 adds sovereign intelligence analysis and open-standards deployment.
- Enterprise Studio unifies Query, Bloom, and Dashboards across self-hosted and AuraDB.
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Benefits
Health Insurance
Dental Insurance
Vision Insurance
401(k) Retirement Plan
Stock Options
Paid Vacation
Growth & Insights and Company News
6 month growth
↑ 0%1 year growth
↑ 0%2 year growth
↑ 0%Neo4j has launched GraphAware Financial Crime Intelligence, a graph-native solution for detecting, investigating, and preventing financial crime in banking and insurance. The announcement follows Neo4j's acquisition of GraphAware in August 2026. The OECD reported $442 billion was lost to consumer fraud globally in 2025, making it the world's second-most common crime after burglary. Financial institutions face increasing regulatory pressure and expensive fines to prevent such crimes. The new solution uses graph databases to connect data silos and identify patterns through multi-hop reasoning, enabling analysts to make faster decisions. Neo4j already provides fraud detection solutions for major financial institutions including BNP Paribas, UBS, and Zurich. The platform offers a complete financial crime investigation cycle built on a knowledge layer for AI, addressing fraud, anti-money laundering, and compliance challenges.
Neo4j intros graph-based suite for fighting financial crime. The vendor is adding tooling from its GraphWise acquisition to go beyond fraud detection and differentiate itself from those providing only part of the crime-stopping ecosystem. Published: 16 Sep 2026 Graph database specialist Neo4j is upping its fight against financial crime. Just over one month after completing the acquisition of GraphAware, which was first agreed upon in June, Neo4j launched a new suite for detecting and stopping fraud that includes capabilities it inherited through the purchase. Neo4j GraphAware Financial Crime Intelligence is a graph-native tool featuring a graph-based knowledge layer purpose-built for banks and insurance companies to use AI to detect and prevent financial crime. Neo4j has provided fraud detection capabilities since launching its first graph database in 2010. However, after detecting potential incidents, customers had to carry out their investigations and analyses using other platforms. GraphAware Financial Crime Intelligence, which combines Neo4j's existing graph-based fraud detection capabilities with complementary capabilities from GraphAware, encompasses the entire financial crime investigation cycle from detection through alerting and investigation to decision. "Detection was only ever half the job," Devin Pratt, an analyst at IDC, told TechTarget. "Putting the full investigation on one graph-native stack removes the handoffs - and the risk - that live in the gaps between systems." Stephen Catanzano, an analyst at Omdia, a division of TechTarget, likewise noted that the significance of Financial Crime Intelligence is that it expands on Neo4j's fraud detection capabilities. "What makes it particularly valuable is the addition of a comprehensive workflow... underpinned by a reusable knowledge layer for trustworthy AI that enables explainable decision-making throughout the entire detection, investigation and prevention process," he told TechTarget. A complete crime-fighting lifecycle. Neo4j is not the only graph database specialist to offer fraud detection capabilities. TigerGraph, PuppyGraph and Memgraph do as well. In addition, broader-based data management vendors such as AWS and Oracle provide fraud detection tools as part of their database offerings. However, Catanzano noted that Neo4j is going beyond what most competing vendors offer by combining fraud detection with investigative and decision-making capabilities in a single feature. "While other vendors may offer fraud detection tools, Neo4j's approach of combining graph-native storage with a reusable knowledge layer that continuously enriches context and enables multi-hop reasoning across the entire investigation workflow positions it distinctively in the market," he said. Pratt similarly stated that Neo4j's differentiation lies in the depth and completeness of Financial Crime Intelligence. In particular, he highlighted the inclusion of an explainable knowledge layer that provides a map of connections between data points and shows how and why the tool's AI capabilities deemed something suspicious and made the recommendations it did. "Plenty of vendors do graph, or do fraud," Pratt said. "Neo4j's bet is owning the whole workflow, with explainability built in." While acquiring GraphAware enabled Neo4j to build a complete financial crimes detection suite, the vendor's impetus for developing Financial Crime Intelligence was also a response to the growing need for customers to show they are taking appropriate steps to fight fraud, according to Michael Down, Neo4j's global head of financial solutions. "With the acquisition, we have the investigation and knowledge layer capabilities to package a... decision workflow, rather than requiring institutions to build it themselves," he told TechTarget. "The timing reflects both the acquisition coming together and a market under real pressure, with regulators pushing harder on institutions to prove proactive prevention, not just after-the-fact detection." Yearend plans. Neo4j's product development plans over the final months of 2026 include expanding and improving its knowledge layer for AI, adding agentic AI capabilities to its platform to aid users, and enabling customers to remain compliant with data sovereignty regulations by enabling them to control where and how their data models run, according to Down. "Financial Crime Intelligence is an early proof point of this approach [to data sovereignty], applying what we built with GraphAware to a specific vertical," he said. "We expect the same pattern to extend into other industries over time." Turning its knowledge layer into a foundation for other industry-specific capabilities is a wise strategy, according to Pratt, who noted that financial services is not the only vertical that benefits from explainable decisions based on connected data. "Build the knowledge layer once for fraud, and the payback comes from every regulated decision it can serve next," he said. Catanzano, meanwhile, suggested that Neo4j could improve its fraud detection and financial crime fighting prowess by adding AI capabilities such as predictive risk scoring and investigation recommendations to help analysts battle growing AI-powered fraud tactics. In addition, like Pratt, he advised Neo4j to expand crime detection capabilities beyond financial services. "Neo4j might extend the platform to regulated industries facing similar networked crime challenges, such as healthcare fraud or supply chain integrity, thereby appealing to new market segments while deepening its position as the go-to graph intelligence platform for complex, relationship-driven risk detection across enterprises," he said. Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management. Related resources.
Vuono Group becomes Neo4j's first partner in Finland - accelerating the shift to agentic AI at scale with knowledge graphs. Helsinki - 12 August, 2026 Vuono Group has become the first Finnish company to join Neo4j's partner network. Neo4j is the world's leading graph intelligence platform, trusted by 84 of the Fortune 100, like Uber, BMW, Airbus and NBC News, to build the data context their AI systems require. The partnership addresses a growing need as organisations look to move beyond AI pilots towards productive solutions and agentic systems. AI solutions cannot succeed without reliable data, the right context, and a thorough understanding of business processes. The challenge grows with the size and complexity of the organisation. "When almost all of the Fortune 100 are already building their AI on Neo4j graph intelligence, the direction is clear. Finnish organisations need to act now. How data and processes are structured determines whether business scales predictably or whether costs and governance spiral out of control", says Sampo Hämäläinen, Founder of Vuono Group. "Neo4j's knowledge graphs are built specifically for this need. We can now combine this with our process expertise to create genuinely productive AI solutions that are ready for enterprise scale." Neo4j's graph intelligence platform gives AI the context it needs, connecting data across systems, relationships, and history, so it reasons from knowledge rather than guessing. And as usage grows, this also matters for cost and performance. Rather than dumping masses of data into the AI's memory just in case, Neo4j retrieves only what is relevant, creating the foundation for controlled and cost-efficient scaling. "Organisations are looking to move beyond disconnected data and create an enterprise knowledge layer that gives AI the context it needs to reason accurately - that's exactly what Neo4j is built for, making AI accurate, explainable, and governed", says Simon Capel, EMEA Head for Channels and Alliances, Neo4j. "We selected Vuono Group as our first Finnish partner because they bring exactly the process expertise needed to turn that knowledge into real business value. They understand that AI is only as good as the knowledge layer that underpins it, and graph is central to that. Nordic organisations now have a partner who can connect the technology to the business process, building AI that is grounded in how their business actually operates." About Vuono Group. Vuono Group is a process AI company that creates superior business processes through data and AI. With a proven track record across Nordic enterprises and public sector organisations, its work spans AI & Data Engineering, Business Engineering, and AI-Driven Software Engineering. About neo4j. Neo4j is the graph intelligence platform that transforms data into knowledge to power the next generation of intelligent applications and AI systems. It includes enterprise-ready knowledge graphs for accurate, explainable, and governed AI; the most comprehensive, trusted, and easy-to-deploy graph database with capabilities across any environment and data source; and an unmatched ecosystem trusted by 84 of the Fortune 100 and supported by the world's largest graph community. Learn more at neo4j.com.
Ortecha and Neo4j Partner to turn connected data into better decisions and more reliable AI. London, UK - 13 July, 2026 Ortecha, the practitioner-led data, AI and technology consultancy, today announced a partnership with Neo4j, the leading graph intelligence platform. By combining Ortecha's expertise in delivering data and AI solutions with Neo4j's graph technology, the two companies will help organisations make sense of how their data actually connects and turn fragmented systems into insights that drive better decisions and more reliable, real-world AI. Most organisations don't have a data problem. They have a connection problem. Data sits across systems, teams and processes with no clear way to understand how it fits together. Graph technology changes that. Neo4j models data as connected relationships, making it possible to see patterns, context and dependencies that traditional approaches miss. It allows organisations to ask and answer questions they simply couldn't before, surfacing insights hidden across customers, operations and complex ecosystems. Together, Ortecha and Neo4j will help clients move from disconnected data to connected intelligence, combining powerful graph technology with hands-on delivery to create solutions that work in practice, not just on paper. The partnership will focus on: * Building AI-ready data foundations through graph-enabled architectures and connected data models * Enabling context-rich AI that is more accurate, explainable and grounded in how the business actually operates * Unlocking new insight from complex relationships, from customer behaviour to operational dependencies * Supporting end-to-end delivery, from strategy and architecture through to implementation and adoption Charles Ivie, Partner, Head of Data & AI Engineering at Ortecha, said: "The Graph database industry wouldn't be where it is today if it wasn't for Neo4j. I am delighted to be once again collaborating with my esteemed friends and colleagues at Neo4j to create cutting-edge Neuro Symbolic AI solutions for our customers." Simon Capel, RVP EMEA Channel & Alliances at Neo4j, said: "Ortecha brings the practitioner depth that turns compelling architecture into solutions that get built and adopted. Their focus on financial services and regulated industries is especially valuable because in these environments, getting AI right is as much a compliance requirement as a competitive advantage. Across EMEA, organisations are looking to move beyond disconnected data and create an enterprise knowledge layer that gives AI the context it needs to reason accurately. Graph is central to that, and together we're helping customers build AI that is more accurate, explainable and grounded in how their business actually operates." This partnership reflects a shared belief that the future of data and AI isn't just about collecting more information or building bigger models. It's about understanding how everything connects. By creating a clearer picture of relationships across the business, organisations can move faster, make better decisions, and build AI that is not only intelligent but grounded in reality. The partnership launches immediately across the UK and EMEA. Organisations looking to improve AI outcomes, connect fragmented data, or solve complex challenges where understanding relationships and context matters can engage with Neo4j and Ortecha to explore use cases and accelerate delivery. About neo4j. Neo4j is the graph intelligence platform that transforms data into knowledge to power the next generation of intelligent applications and AI systems. It includes enterprise-ready knowledge graphs for accurate, explainable, and governed AI; the most comprehensive, trusted, and easy-to-deploy graph database and capabilities across any environment and data source; and an unmatched ecosystem trusted by 84 of the Fortune 100 and supported by the world's largest graph community. Learn more at neo4j.com. About Ortecha. Founded in 2010, Ortecha is a human-first data, AI and technology consultancy helping complex organisations turn strategy into operational reality. Ortecha specialises in data management, governance, enterprise architecture, AI readiness and transformation delivery across regulated and enterprise-scale environments. Ortecha's teams are made up of practitioners who have built, delivered and scaled enterprise technology first-hand, bringing practical experience to every engagement. Talk to its experts. Ortecha Ltd'd love to connect and find out more about your strategy and challenges.
This Week in Neo4j: GraphAcademy takeover. Developer Experience Engineer at Neo4j July 10, 2026 Hey, Adam (from GraphAcademy) here, filling in for Alex this week. This week, in This Week in Neo4j, I'm here to let you know that Neo4j Inc has launched a brand new, refreshed version of GraphAcademy. For those of you who aren't aware, it's Neo4j's home for hands-on learning. Real Cypher, real graphs, completely free hands-on learning. You learn by building, either at your own pace or live in a workshop with other people. Over the past few months, Neo4j Inc has been busy rebuilding the site from the ground up. Neo4j Inc is bringing E.L.A.I.N.E, its AI learning assistant, to the front and center, to help you find your next course and guide you through your learning. If you live in your IDE, Neo4j Inc has also released a GraphAcademy MCP server, so you don't need to leave your editor to learn Neo4j. Neo4j Inc has also been busy writing for its new blog feature. But that's not what this email is about. This one's about what's new to do. Share your experiences and influence the future of Neo4j products: Join the Neo4j User Research panel! It's a chance to connect directly w,ith product development teams, get paid compensation, hear about what Neo4j Inc is working on and more! Happy Graphing, Adam Cowley Coming up! * Livestream: Network Intrusion Detection with Neo4j and Snowflake on July 22 * Meetup: Meet Neo4j Inc in Pune, IN on July 18 & Melbourne, AU on July 23 * All Neo4j Events: Webinars and More FEATURED COMMUNITY MEMBER: Nivedita Thapa. Nivedita built Semantic Model Inspector, an open-source tool that evaluates how ready enterprise semantic models are for LLM-powered analyst tools like Snowflake Cortex Analyst. Her work sits at the intersection of semantic modeling, knowledge graphs, and AI-powered analytics. Connect with her on LinkedIn. She has one of the first confirmed sessions at NODES 2026 "Are Your Semantic Models AI-Ready? What Knowledge Graphs Teach Us About Building Context for LLMs", where she will take a public semantic model, encode the same domain in Neo4j as a typed knowledge graph, and expose that graph context to an LLM before SQL generation. You will see the graph schema, the Cypher patterns that capture what warehouse-native semantic layers miss, and side-by-side code examples of LLM query generation with and without graph-backed context. FOCUSSED LEARNING: Try a lab now. Some things don't need a full course. You just need to learn one thing, fast. That's what Labs are designed for - short, single-module, hands-on lessons that get you in and out with one specific skill. One subject. No setup. Less than an hour. A few to start with: * Working with Dates and Durations in Cypher - learn how temporal types, including dates and times, can be used in Neo4j to filter, compare, and calculate with precision. * Graph type Schema Enforcement - define a consistent schema that acts as your ontology at the database level. Structure without giving up flexibility. * Full-Text Search in Neo4j - Create and query full-text indexes for case-insensitive search. Try one now. You'll be skilled and ready in time for your morning standup. Not sure what to learn next? There's more than one way to find out. Courses are now organized by: * Topic - GraphRAG, Cypher, MCP, and more. * Persona - developer, data scientist, DevOps, context engineer, graph data scientist. * Learning path - a sequence of courses aimed at a concrete goal. One example of a learning path is Neo4j certification: a defined route that ends with something real, a certified Neo4j developer credential. Got something specific in mind? Tell the onboarding assistant what you're trying to build, right from the homepage search box, and it'll point you to the courses or path that get you there. Choose your own adventure. Self-paced learning gets you far, but nothing beats building something live, with other people, in the room (or on the call) with you. And as more of its workshops get built around MCP, that hands-on format matters even more: you're learning how to work with Neo4j inside the tools you already use. Case in point: the Agentic GraphRAG Mini Hack in Bengaluru, run by Zaid Zaim. Thirty minutes of teaching, two hours of MCP-assisted coding, seventeen projects built, four winners. Would you like to deliver one of these yourself? If you know Neo4j and want to teach it - in your workplace, at a meetup, wherever - Neo4j Inc want to help you do it. Deliver through the GraphAcademy platform, and you get instructor notes, a built-in slide view, and help promote the event. Neo4j Inc'll even run a train-the-trainer session with you first, so you walk in confident. Show up, log in, present. That's the takeover. New site, new Labs, clearer paths to your next course, and more workshops than ever. Neo4j Inc put a lot of time and thought into this platform, from the design down to how each course teaches you something. So go take a look, and tell Neo4j Inc what you think. What's working, what's not, what would make it better. Neo4j Inc is genuinely excited to hear it. And if you're ready to teach Neo4j yourself, get in touch. Neo4j Inc'll help you get there. See you at a workshop soon! EcocomityChain.AI built a material genealogy graph in Neo4j that traces a modeled vehicle from the finished product down to raw ore. Eight levels deep, 34,713 nodes, 554 suppliers across every tier. The payoff is the kind of question a flat supplier list can't answer. Their graph spots when the same nickel ore sits under 19 separate assembly chains, quietly turning one mine into a single point of failure for an entire vehicle program. It also surfaces where your real recovery time hides, often six levels below your Tier-1 supplier. They wrote it up as three worked scenarios against a live graph, so it reads like a build log you can actually follow. If you're wrestling with deep, connected data of your own, it's a sharp example of what graph thinking makes visible. Continuous learning. * GraphAcademy: Join the "Cup", complete Courses and win prizes * Learn on Your Schedule: Go deeper into graph intelligence on Neo4j's On-Demand webinar library * Workshops: Join its virtual classrooms workshops from Fundamentals to GenAI * New Webinar: Enterprise AI is missing a key ingredient: The knowledge layer - Americas, Europe, Middle East & Africa, Asia Pacific