Security Automation Lead Jobs

Databases and data stores are at the center of our applications, for legacy applications and modern AI applications alike. Yet most observability and optimization approaches lack a holistic approach or application context. Datadog has been on a mission to revolutionize how databases are operated, flipping what is often seen as a black box of complexity prone to security and performance risks, into a well oiled machine enabling our builders and businesses to move faster and smarter. We’re looking for an experienced product manager passionate about joining this mission to lead this product opportunity. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Lead ambitious investments that rethink how customers operate and get value from their databases, diving into ambiguity, working with our customers on new models, and shipping new products and changes to existing products. Develop a deep understanding of our customers and their issues, what problems are really behind those issues, and how we can improve how databases are operationalized across SRE teams, DBAs and application developers. Continuously refine your understanding of database management systems and datastores from SQL and OLTP based to NoSQL e.g. AWS RDS, PostgreSQL, SQL Server, MongoDB, MySQL, etc Define, build and launch the next generation of database monitoring and optimization capabilities for our customers Join a talented engineering team with a record of disrupting observability approaches to further the mission of demystifying and optimizing databases using your team’s creativity, alongside your customers’ problems, as a key resource. Collaborate with other Product teams in Datadog to maintain and improve all Datadog products, improve the seamless integration across th

The Documentation team creates engaging and informative technical content, particularly our public product documentation: Datadog Docs . This is an opportunity for a Documentation Manager to help us deliver high quality technical documentation and lead one of our growing documentation teams. Our team is hands-on with the technologies that Datadog monitors, and collaborates with technical and product teams to create technical documentation for our APIs, SDKs, developer and security tools, and community developed integrations — thousands of pages of content! This role is remote in North America. What You’ll Do: Manage 3-5 technical writer direct reports, overseeing their day-to-day, helping them make and manage effective content project plans, formally supporting their growth and development, and fostering an environment that encourages positive engagement and performance. Partner with our Product and Engineering teams to create and maintain public documentation for users, developers, and SREs, helping them succeed with Datadog products, features, SDKs, APIs, and developer tools. Experiment with the product, dig into source code, and interview subject matter experts to research technical details for documentation. Collaborate with our contributor community and greater Documentation team to develop and improve documentation standards and processes. Who You Are: You have 5-10 years experience researching and developing documentation for technical topics, like databases, APIs, cloud infrastructure, and performance and security monitoring. You have 2-5 years experience managing a small team of technical writers, coaching their performance, guiding their career journey, and breaking down complex projects into achievable plans. You have publicly available technical writing samples. You are comfortable reading at least two programming languages (e.g. Ruby, Python, Go, bash). You have written or managed documentation as code, and are familiar with modern infrastructure such a

MongoDB is seeking an Engineering Manager to join the Atlas Organization. The organization is responsible for building MongoDB Atlas, our database-as-a-service offering and fastest growing product. Atlas allows users to deploy fault-tolerant, secure, globally distributed MongoDB clusters in just minutes. This includes developing software to interface with the three major cloud providers (AWS, Azure, and GCP) in order to bring security, durability, availability, and performance to all deployments of MongoDB. The Atlas Data Federation & Archiving team is an engineering team responsible for the Atlas capabilities that allow customers to move data from hot to cold storage and run federated queries over that data. The team builds Atlas Data Federation, a distributed query engine that lets users query data across Atlas Clusters and cloud object storage through a unified service. The team also builds Atlas Online Archive which allows customers to move data from Atlas Clusters into fully managed cloud object storage while preserving a seamless query experience across hot and cold datasets. We are forming a new Atlas Data Federation & Archiving team in the Dublin area. The Engineering Manager who fills this position will be pivotal in growing that team. We are looking to speak to candidates who are based in Cork and would like a hybrid or in-office working model. What you’ll do Lead a team of motivated individual contributors who are eager to learn and grow Contribute to the code, design, and architecture of the systems your team develops Work with stakeholders throughout MongoDB to build our roadmap and product offerings Work with customers and support engineers to fix issues and become part of our on-call rotation Collaborate with team members to develop our codebase, best practices, and design principles Foster an inclusive and respectful work environment according to MongoDB's Core Values We’re looking for someone who Has at least 6 years of professi

About Datadog: Datadog is a best-in-class SaaS business, delivering a rare combination of growth, profitability, and stock market appreciation. Built by engineers, for engineers, our monitoring and security platform is used by organizations of all sizes across a wide range of industries to enable digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stack. We’re dedicated to creating, developing, and supporting our product and customers, allowing for seamless collaboration and problem-solving among Dev, Ops, and Security teams globally. Come join the team for an exciting opportunity to learn from top-level leaders and colleagues and grow alongside the company as we rapidly expand. The Team: Our sales team works with a best-of-breed product that solves real problems for our customers. Sellers follow a well-defined methodology that helps them identify the customer's unique needs and clearly convey the value of the Datadog product. Whether you're looking to learn from the best or be the best, the Datadog sales team is dedicated to furthering personal development and team success. The Opportunity: Datadog is the monitoring and analytics platform for developers, IT teams and business users in the cloud age. We're scaling our Inside Sales team to help IT and Technology leaders recognize Datadog’s impact in their digital transformation and migration to the cloud. This is an opportunity to bring a proven product, loved by thousands of customers, to a multi-billion dollar market. You Will: Focus 100% on net new logo acquisition Manage the full sales cycle Run and partner with Sales Engineers on demos Use tools such as Sales Navigator and ZoomInfo Strategically prospect into CTOs, Engineering/IT Leaders, & technical end users You Are: Focused on hunting and closing net new logos A top performer with a history of success Bold on the phones, and creative in your emails Able to strategic

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure Design and build simulated environments and RL training loops for on-policy agent training and evaluation Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products Stay at the forefront of foundation models, world models, and RL-based agent research Contribute to r

The Data Engineering team is responsible for building ETL pipelines that populate the Internal Data Platform, which drives analytics that help the company run more efficiently. Our team builds highly performant and scalable processes that extract massive datasets and makes those datasets available for querying in an optimal way. We are looking to speak to candidates who are based in Gurgaon for our hybrid working model. What you’ll do Guide the Data Engineering team on building highly performance ETL pipelines using Spark and other Big Data technologies Help design the architecture of our Internal Data Platform to support the implementation of a robust medallion architecture Provide thought leadership on ways to achieve infrastructure cost savings on Cloud hyperscalers Design and build AI agents that can help automate many of the common development and support tasks that the team performs Work with Security and Compliance teams to ensure that datasets have appropriate permissions and regulations in place Work with our Data Platform, and Governance sibling teams to make data scalable, consumable, and discoverable We’re looking for someone with 10+ years experience working on enterprise data lakes/warehouses 5+ years of Spark and Python experience 5+ years of direct hands-on experience working with AWS or GCP Thorough AI knowledge, particularly with codegen tools and agentic frameworks Hive, Iceberg, Glue, or other technologies that expose big data as tables Familiarity with different big data file types such as Parquet, Avro, and JSON Exposure to real-time or streaming data technologies is a plus Success Measures In 3 months, you'll have a thorough understanding of the architecture of MongoDB’s internal Data and AI ecosystem In 6 months, you'll have owned the delivery of a large project from start (scoping, design) to finish (delivery) In 12 months, you'll have designed new features, led development work, and become a go-to expert on parts of the system About MongoDB

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure Design and build simulated environments and RL training loops for on-policy agent training and evaluation Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products Stay at the forefront of foundation models, world models, and RL-based agent research Contribute to r

We are seeking a Staff engineer to design, build, and operate the internal and external Observability stack for the MongoDB platform. Tens of thousands of customers depend on our Observability stack to monitor their database clusters and to generate actionable alerts to safeguard critical workloads. This is an opportunity to join a team that is responsible for all Observability systems that support metrics, metric visualization, logs, traces, and alerts for MongoDB. We are looking for engineers with high standards, and experience in setting direction and technical leadership for large engineering teams in designing and operating complex distributed systems, with strict SLO on security, durability, availability and performance. As MongoDB Atlas and its supporting infrastructure continue to experience rapid growth, the demand for high-cardinality observability data for internal and external use cases means we need to continually innovate and scale our systems to the next level. For example, MongoDB Observability systems need to handle 10’s of billions of metrics time series, all whilst processing petabytes of logs, traces, and events. Our stack includes VictoriaMetrics, Splunk, Flink, WarpStream/Kafka, Java, Golang Fluentbit. In addition to owning critical components of our observability infrastructure, as a Staff engineer on the team, you’ll also work closely with other SWE, Product and SRE teams to promote and implement best practices in instrumenting and monitoring their services. This is a highly collaborative role, and you will get to own some of the most relied upon internal infrastructure at Mongo. Our team champions a strong culture of inclusivity, diversity, and collaboration. If you want to be a deeply technical leader on a collaborative team that applies low-level systems expertise to build the foundational infrastructure of a popular database, join us! Let’s build a faster, more reliable, and exceptionally observable database system together. W

MongoDB is seeking an Engineering Manager to join the Atlas Organization. The organization is responsible for building MongoDB Atlas, our database-as-a-service offering and fastest growing product. Atlas allows users to deploy fault-tolerant, secure, globally distributed MongoDB clusters in just minutes. This includes developing software to interface with the three major cloud providers (AWS, Azure, and GCP) in order to bring security, durability, availability, and performance to all deployments of MongoDB. The Atlas Data Federation & Archiving team is an engineering team responsible for the Atlas capabilities that allow customers to move data from hot to cold storage and run federated queries over that data. The team builds Atlas Data Federation, a distributed query engine that lets users query data across Atlas Clusters and cloud object storage through a unified service. The team also builds Atlas Online Archive which allows customers to move data from Atlas Clusters into fully managed cloud object storage while preserving a seamless query experience across hot and cold datasets. We are forming a new Atlas Data Federation & Archiving team in the Dublin area. The Engineering Manager who fills this position will be pivotal in growing that team. We are looking to speak to candidates who are based in Dublin and would like a hybrid or in-office working model. What you’ll do Lead a team of motivated individual contributors who are eager to learn and grow Contribute to the code, design, and architecture of the systems your team develops Work with stakeholders throughout MongoDB to build our roadmap and product offerings Work with customers and support engineers to fix issues and become part of our on-call rotation Collaborate with team members to develop our codebase, best practices, and design principles Foster an inclusive and respectful work environment according to MongoDB's Core Values We’re looking for someone who Has at least 6 years of profes

As a Research Engineer on our team, you will partner with Research Scientists to turn research ideas into working systems, building the data, tooling, and infrastructure that enable rapid iteration, trustworthy evaluation, and a smooth path from prototype to production. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Build and operate multimodal data pipelines, training and evaluation infrastructure, benchmarks, and internal tooling Implement models, run experiments at scale, and profile for reliability, performance, and cost Build simulation environments and replay infrastructure for agent training and evaluation Orchestrate distributed training and distributed RL with Ray, including scheduling, scaling, and failure recovery Establish rigorous automated benchmarks and regression tests for world model predictions, agent performance, and simulation fidelity Collaborate with Research Scientists, Product, and Engineeri

MongoDB is seeking a Staff Software Engineer to join the Atlas Clusters Organization. The organization is responsible for building MongoDB Atlas, our database as a service offering and fastest growing product. Atlas allows users to deploy fault-tolerant, secure, globally distributed MongoDB clusters in just minutes. This includes developing software to interface with the three major cloud providers (AWS, Azure, and GCP) in order to bring security, durability, availability, and performance to all deployments of MongoDB. This engineer will also work on our Atlas Data Federation & Archiving product. Atlas Data Federation & Archiving allows customers to move data from hot to cold storage and run federated queries over that data. We are forming a new Atlas Clusters team in the Dublin area. We are looking to speak to candidates who are based in Dublin and would like a hybrid or in-office working model. What you’ll do Build and design new features for MongoDB Atlas and Atlas Data Federation & Archiving Contribute to and lead complex technical projects Work with stakeholders throughout MongoDB to build our roadmap and product offerings Work with customers and support engineers to fix issues and become part of our on-call rotation Collaborate with team members to develop our codebase, best practices, and design principles Foster an inclusive and respectful work environment according to MongoDB's Core Values We’re looking for someone who Has at least 10+ years of professional software development experience Is skilled at writing large-scale, distributed backend systems in a compiled language (Go, Java, C#, etc) Has experience with at least one major cloud provider technology (AWS, Azure, GCP) Has led the launch of a new module and maintained it in production Is eager to solve tough problems Has excellent communication skills Is curious, collaborative, and motivated Success Measures In 3 months, you'll have shipped code into production and c

About the Role We are seeking a Staff Enterprise Architect, Data to lead the strategy, design, and modernization of our enterprise data landscape. This role operates at the intersection of data architecture, engineering, and AI enablement, defining solutions to integrate our Data Lake and Data Warehouse across multi-cloud platforms. Over the next 12-18 months, you will enable self-service data access and natural language query capabilities for business users. You will architect Master Data Management and data lineage frameworks ensuring AI models operate on high-quality, governed data. You will also evaluate and implement AI-powered tools to automate data quality monitoring and enhance data security. We're looking to speak with candidates based in the San Francisco Bay Area for our hybrid working model. Key Responsibilities Data Strategy & Roadmap Design semantic layer architecture standardizing business metrics enterprise-wide. Define governance guardrails ensuring natural language queries access validated master data sources Develop Master Data strategy for Customer and Product domains (phases 1-2), Finance and People to follow. Define golden record requirements, stewardship models, and system-of-record hierarchy. Partner with business owners on master data governance Define cross-cloud data integration strategy and reference architecture. Specify patterns (federation, replication, abstraction layer) balancing performance, cost, and data freshness. Document trade-offs and recommend implementations for batch and near-real-time use cases Develop 12-24 month data architecture roadmaps for Finance, Sales, Product, and People. Identify capability gaps and recommend technology investments with business value and effort estimates Systems Design & Solution Leadership Evaluate AI-powered data observability platforms for quality monitoring, pipeline failure prediction, and data classification. Define requirements, lead vendor POCs, and establish integration patterns

The Application Modernization Platform (AMP) team is tackling one of the industry's most critical challenges: leveraging Generative AI to transform rigid, legacy applications into modern, microservices-based architectures powered by MongoDB. We are building a comprehensive, SaaS-like platform, encompassing both the "brain" (multi-agent reasoning and orchestration) and the "hands" (the deployment platform and modernization toolset). This solution requires a robust platform foundation and infrastructure designed for a "build once, run anywhere" model, ensuring seamless operation regardless of a client's security or network constraints. A key challenge is balancing the need to tune our tools for each customer's unique tech stack and restrictive environments with making them easily extensible and scalable for common application modernization challenges. We seek an engineering leader for this high-visibility initiative. This role requires defining the high-level strategy and technical direction across all AMP engineering pillars, leading the execution of solving uniquely complex application modernization puzzles, and delivering an enterprise-grade product. The leader will minimize deployment friction, meet customer compliance requirements, and help shape the future of how global enterprises leverage GenAI. The ideal candidate is a hands-on technical leader who excels at leveraging GenAI capabilities, architecting complex distributed systems, and designing the orchestration agents necessary to reliably and fluidly run the entire software development lifecycle. This role will be based in North America's West Coast (PST), and offers a hybrid working model. The ideal candidate for this role will have 10+ years of software development and operations experience, with a focus on building platforms and distributable software infrastructure Deep experience in building data warehouses and core components for data processing systems Have experience in using GenAI in building comple

The Application Modernization Platform (AMP) team is tackling one of the industry's most critical challenges: leveraging Generative AI to transform rigid, legacy applications into modern, microservices-based architectures powered by MongoDB. We are building a comprehensive, SaaS-like platform, encompassing both the "brain" (multi-agent reasoning and orchestration) and the "hands" (the deployment platform and modernization toolset). This solution requires a robust platform foundation and infrastructure designed for a "build once, run anywhere" model, ensuring seamless operation regardless of a client's security or network constraints. A key challenge is balancing the need to tune our tools for each customer's unique tech stack and restrictive environments with making them easily extensible and scalable for common application modernization challenges. We seek an engineering leader for this high-visibility initiative. This role requires defining the high-level strategy and technical direction across all AMP engineering pillars, leading the execution of solving uniquely complex application modernization puzzles, and delivering an enterprise-grade product. The leader will minimize deployment friction, meet customer compliance requirements, and help shape the future of how global enterprises leverage GenAI. The ideal candidate is a hands-on technical leader who excels at leveraging GenAI capabilities, architecting complex distributed systems, and designing the orchestration agents necessary to reliably and fluidly run the entire software development lifecycle. This role will be based in North America's West Coast (PST), and offers a hybrid working model. The ideal candidate for this role will have 10+ years of software development and operations experience, with a focus on building platforms and distributable software infrastructure Deep experience in building data warehouses and core components for data processing systems Have experience in using GenAI in building comple

The Team Platform Engineering is the department within SRE that is responsible for a range of critical infrastructure and operational functions that support the broader engineering organization. Among these are our multi-cloud-provider Kubernetes infrastructure, networking, load balancing (including our public-facing edge and internal service mesh), and observability and alerting systems. The Fleet Management team provides the core runtime environment that empowers our developers to build and ship products to delight our customers. We manage the end-to-end lifecycle of our Kubernetes fleet, alongside the critical components that ensure cluster reliability and security (e.g., CoreDNS, cert-manager, and Gatekeeper). As our infrastructure scales to support new use cases and products, we are spearheading a migration from Terraform-based Infrastructure as Code (IaC) to an Operator-driven lifecycle management model. This role can be based out of our Austin, Boston, Los Angeles, New York City, Raleigh, or San Francisco offices, remotely in the United States region, or our European office in Dublin. Responsibilities Contribute to developing and maintaining a scalable and secure runtime environment on top of Kubernetes that supports product needs across MongoDB Provide internal support for our Kubernetes ecosystem, partnering with engineering teams to help them solve domain-specific problems Participate in a 24/7 on-call rotation to resolve critical issues Prioritize blameless post-mortems and dedicate engineering time to systemic fixes, ensuring you aren’t paged for the same issue twice You may be a good fit if you Have 6+ years of experience in software development and operating distributed systems Are proficient in Go, Python, or a similar language, with a strong commitment to code quality and testing practices (writing unit, integration, and E2E tests) Have deep experience using and extending containerization technologies, preferably Kubernetes Have a solid understanding