Full-Time

Vice President

Global Enterprise Sales Engineering

Posted on 10/31/2025

Datadog

Datadog

5,001-10,000 employees

Cloud monitoring, analytics, and observability platform

Compensation Overview

$260k - $380k/yr

Company Historically Provides H1B Sponsorship

San Francisco, CA, USA + 1 more

More locations: New York, NY, USA

Hybrid

US Top Secret Clearance Required

Category
Sales & Account Management (1)
Sales & Solution Engineering (1)
Required Skills
DevOps
Requirements
  • 10+ years of experience leading global enterprise technical pre-sales or enterprise sales engineering teams in high-growth, global B2B SaaS organizations
  • Proven ability to scale and operate a global Sales Engineering organization of 300+, with teams distributed across regions and customer segments (Commercial, Mid-Market, and Enterprise)
  • Track record of building and leading world-class, customer-facing technical teams with operational excellence at scale
  • Deep experience in technical pre-sales, including solution engineering, value selling, technical discovery, product demos, proof-of-value (PoV) execution, and competitive positioning
  • A hands-on, servant leader who builds trust across all levels of the organization and leads with humility, kindness, and a team-first mindset
  • Experience navigating and leading teams through change management and evolving operating models in dynamic, high-growth environments
  • Strong decision-making abilities with a proven ability to balance strategic priorities and frontline realities in fast-paced, matrixed environments
  • Analytical and data-driven, with a deep understanding of performance metrics, capacity planning, funnel conversion, and productivity modeling
  • Solid technical foundation and strong business acumen; able to map complex customer challenges to Datadog’s multi-product platform
  • Exceptional communication skills, with a proven ability to engage effectively with executive stakeholders, customers, and cross-functional partners
  • Experience in SaaS required; knowledge of observability, cloud infrastructure, DevOps, or security domains strongly preferred
  • Bachelor’s degree in Engineering or a related technical field required; advanced degree preferred
Responsibilities
  • Set a strategic, long-term plan for the global ESE organization
  • Define and execute the global strategy for the Enterprise Sales Engineering (ESE) organization, aligning with Datadog’s broader enterprise growth objectives
  • Lead annual and quarterly planning cycles to set a proactive, forward-looking agenda that accelerates the enterprise sales motion and positions the team for long-term success
  • Establish and communicate clear KPIs, benchmarks, and priorities, ensuring transparency and alignment across the ESE organization
  • Develop a long-term vision and three-year strategic roadmap, grounded in industry trends, evolving customer needs, and Datadog’s multi-product trajectory
  • Set the bar for technical excellence across all enterprise sales engagements, ensuring every customer interaction reflects Datadog’s high standards of quality, clarity, and value
  • Own the quality and consistency of all technical engagements with enterprise customers, from discovery to validation, ensuring alignment with customer needs and strategic goals
  • Define and uphold performance standards across the organization; your expectations will shape the culture and execution of the entire team
  • Lead and support high-priority customer escalations, providing clear, confident communication and resolution while reinforcing trust with key stakeholders
  • Drive operational improvement initiatives, establishing scalable systems, processes, and metrics that align with Datadog’s strategic goals and improve execution across teams
  • Implement consistent processes and best practices across global regions and teams, ensuring repeatability, alignment, and operational excellence at scale
  • Foster a high-performance culture by empowering individuals and teams to do their best work, consistently raising the bar for technical excellence and customer impact
  • Anticipate and scale organizational capacity to match Datadog’s expanding platform and rapidly growing enterprise customer base
  • Provide inspirational leadership and coaching to AVPs, Senior Directors, Directors, and multi-level managers across global regions, driving productivity, engagement, and a focus on customer success
  • Develop and support talent by enabling individuals to grow, deliver exceptional outcomes, and reach their full potential
  • Recruit, develop, and retain a diverse, high-performing team of ICs and leaders; invest in thoughtful career coaching and create an environment where others can do the same for their teams
  • Build trust and drive accountability by fostering a culture of transparency, ownership, and continuous improvement
  • Anticipate the growth of teams and leaders to match the increasing breadth of the platform and the increasing number of customers.
  • Provide motivational leadership and coaching across the directors and multiple levels of managers within different regions, to ensure optimal productivity and customer success.
  • Empower individuals and teams to produce high-quality work and reach their own potential.
  • Recruit, attract, and retain a diverse and accomplished team of ICs and leaders. Provide thoughtful, tangible career coaching to your own leaders and enable them to do the same for their teams.
  • Establish trust and foster a sense of accountability and transparency
Desired Qualifications
  • Advanced degree (MS/PhD) in a technical field preferred
  • Experience in SaaS required; knowledge of observability, cloud infrastructure, DevOps, or security domains strongly preferred
  • Bachelor’s degree in Engineering or a related technical field required; advanced degree preferred

Datadog provides a platform for monitoring and analyzing IT infrastructure, including servers, databases, and applications. The product works by collecting data from a user's cloud environment and displaying it in a single dashboard where teams can track performance, manage logs, and detect security threats. Unlike many competitors that offer fragmented tools, Datadog integrates monitoring, security, and analytics into one unified interface with a flexible pricing model based on data usage. The company's goal is to provide organizations with real-time visibility into their digital operations to ensure their systems remain reliable and secure.

Company Size

5,001-10,000

Company Stage

IPO

Headquarters

New York City, New York

Founded

2010

Simplify Jobs

Simplify's Take

What believers are saying

  • FedRAMP High certification expands addressable market to U.S. government clients requiring stringent security.
  • India expansion with 100+ Bengaluru employees and 40 partnerships taps fast-growing cloud adoption.
  • Net revenue retention in low 120% range with 4,550 customers above $100K ARR.

What critics are saying

  • OpenTelemetry standardization enables customers to migrate to free alternatives, eroding lock-in dependency.
  • Grafana's open-source GPU dashboards capture market share at lower cost without host-based pricing.
  • India's DPDP Act enforcement mandates data localization, disrupting 40-partner expansion by Q3 2026.

What makes Datadog unique

  • AI-integrated customers represent 80% of ARR despite being only 20% of base.
  • Purpose-built LLM Toto powers autonomous observability used by 60 of top 100 AI companies.
  • GPU Monitoring detects idle GPUs and zombie processes, saving tens of thousands monthly.

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Benefits

Health Insurance

Dental Insurance

Mental Health Support

401(k) Retirement Plan

401(k) Company Match

Company Equity

Employee Stock Purchase Plan

Professional Development Budget

Hybrid Work Options

Flexible Work Hours

Paid Vacation

Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

0%

2 year growth

2%
Yahoo Finance
Apr 9th, 2026
Guggenheim upgrades Datadog to buy with $175 target after 14% YTD pullback

Guggenheim has upgraded Datadog to Buy with a $175 price target, viewing the stock's 14.33% year-to-date decline as an attractive entry point. The upgrade centres on Datadog's positioning at the intersection of cloud migration and AI deployment. Datadog reported strong fourth-quarter fiscal 2025 results, with revenue of $953.19 million beating estimates by 3.76%. Full-year revenue reached $3.43 billion, up 28% year-over-year. The company now has 603 customers generating over $1 million in annual recurring revenue, up 31% year-over-year. The cloud monitoring platform provider's shares currently trade near $114, well below the 52-week high of $201.69. Management has guided fiscal 2026 revenue to between $4.06 billion and $4.10 billion. Forty-three analysts rate the stock a Buy.

Yahoo Finance
Apr 9th, 2026
Datadog launches Experiments platform as shares trade 36% below analyst target of $181

Datadog has launched Datadog Experiments, an integrated platform for product testing and analytics that lets teams design, launch and measure experiments alongside real-time observability and business metrics. The offering targets enterprises previously relying on separate tools for experimentation, analytics and monitoring. The launch comes as Datadog shares trade around $116.50, down 12.9% year-to-date despite a 19.6% return over the past year. Shares currently trade approximately 36% below the consensus analyst price target of $181.52. By tying experimentation directly to observability and business metrics, Datadog aims to deepen its platform's role in customer workflows. However, profit margins have declined to 3.1% from 6.8% last year, and recent insider selling has been significant. Adoption rates among large customers will be key to watch.

Yahoo Finance
Apr 5th, 2026
Benchmark sets $150 target for Datadog on AI observability strength

Benchmark has initiated coverage of Datadog with a Buy rating and $150 price target, citing the company's AI-powered observability and security platform as positioned to benefit from digital transformation, cloud migration and agentic AI growth. The firm highlighted Datadog's technological leadership, a total addressable market exceeding $400 billion, and consistent profitable growth with Rule of 45+ performance metrics. On 9 March, Datadog announced the general availability of its MCP Server, enabling developers to integrate real-time observability data into AI-driven development workflows. The platform allows teams to debug and operate systems using live telemetry whilst maintaining governance and security controls. Datadog's cloud observability platform is seeing increased adoption driven by AI applications and large language models, positioning it for sustained growth and market leadership.

Nurbak
Apr 2nd, 2026
Honeycomb vs Datadog: which observability tool in 2026?

Honeycomb vs Datadog: which observability tool in 2026? An honest comparison of Honeycomb and Datadog for observability. Honeycomb excels at event-driven debugging. Datadog is the all-in-one SaaS. Here is when to pick each - and when neither fits. Honeycomb and Datadog are both observability tools, but they approach the problem from fundamentally different directions. Honeycomb was built to answer unknown questions about your systems. Datadog was built to be the single platform for everything operations-related. This comparison covers how they actually differ in 2026, where each one excels, and - importantly - when neither is the right choice. Honeycomb: event-driven debugging. Honeycomb was founded by Charity Majors and Christine Yen, both from Facebook's infrastructure team. The core idea: traditional monitoring tools force you to decide what to measure before you know what questions you will ask. Honeycomb flips this. You send high-cardinality events, and query them later. How it works. Every span, log, or event you send to Honeycomb is stored in a columnar store optimized for ad-hoc queries. You can group by, filter, and break down on any attribute - user ID, shopping cart size, feature flag variant, database query text - without pre-defining indexes. This is the key differentiator. In Datadog, if you want to filter APM data by a custom attribute, you need to index it (and pay for it). In Honeycomb, every attribute is queryable by default. Standout features. * BubbleUp: Select a group of slow or erroring requests, and Honeycomb automatically identifies which attributes are different between the selected group and the baseline. Instead of guessing root causes, the tool shows you. * Query builder: Flexible enough to replace many custom dashboards. Group by multiple dimensions, calculate percentiles, heatmaps, and rates - all in a single query interface. * SLOs: Define service-level objectives tied to your trace data. Honeycomb tracks burn rate and alerts when you are consuming your error budget too fast. * OpenTelemetry native: First-class OTLP support. Honeycomb was an early and active contributor to the OpenTelemetry project. Pricing (2026). * Free: 20M events/month * Pro: $130/month for 100M events * Enterprise: Custom pricing, SSO, advanced roles Limitations. * Not an all-in-one platform. No infrastructure monitoring, no synthetics, no log management (though it can ingest structured logs as events). * Steeper learning curve. Getting value from Honeycomb requires understanding high-cardinality querying, which is a different mental model than dashboards-and-alerts. * Smaller ecosystem. Fewer integrations, fewer pre-built dashboards, fewer community resources than Datadog. Datadog: the all-in-one platform. Datadog started as infrastructure monitoring in 2010 and has expanded into a comprehensive observability and security platform. In 2026, it covers infrastructure, APM, logs, synthetics, real user monitoring (RUM), security, CI/CD visibility, database monitoring, and more. You install the Datadog Agent on your hosts (or use serverless integration). The agent collects metrics, traces, and logs automatically. Datadog's auto-instrumentation libraries handle most popular frameworks, so you get APM data with minimal code changes. Everything lands in a single platform with cross-linking: click on a trace to see related logs, jump from a metric spike to the traces that caused it, or correlate infrastructure metrics with application performance. * Unified platform: Infrastructure, APM, logs, RUM, synthetics, security - all in one UI with cross-linking between signals. * Auto-instrumentation: Datadog's agent auto-instruments most frameworks. Less manual work than OpenTelemetry-based tools. * Service map: Automatically generated dependency graph showing how services communicate, with health indicators on each edge. * Notebooks and dashboards: Rich visualization with team sharing, annotations, and incident timelines built in. * Watchdog AI: Automated anomaly detection that flags unusual patterns without manual threshold configuration. * Infrastructure: $15/host/month * APM: $31/host/month * Logs: $0.10/GB ingested (after plan inclusion) * Indexed Spans: $1.70 per million (after 1M included with APM) * Synthetics: $5/1000 API test runs The complexity of Datadog pricing is itself a feature. Or a bug, depending on your perspective. Many teams report bill shock after scaling up, because each product has separate metering and the costs compound. * Cost unpredictability at scale. The per-host, per-GB, per-million-spans pricing model makes budgeting difficult. * Vendor lock-in. Datadog's proprietary agent and query language make migration expensive. * Jack of all trades. Each individual feature is good but rarely best-in-class. Honeycomb's debugging is deeper. Grafana's dashboards are more flexible. PagerDuty's alerting is more sophisticated. Head-to-Head comparison. | Dimension | Honeycomb | Datadog | | Philosophy | Debug unknown unknowns | Monitor everything in one place | | Query power | Excellent (high-cardinality native) | Good (requires indexing for custom attributes) | | Infrastructure monitoring | No | Yes (core strength) | | Log management | No (events only) | Yes | | Synthetics | No | Yes | | Auto-instrumentation | Via OpenTelemetry | Proprietary agent (more automatic) | | SLO tracking | Yes (built-in) | Yes (built-in) | | Free tier | 20M events/month | 5 hosts, limited features | | Cost at scale | Predictable (event-based) | Unpredictable (multi-axis metering) | | Best for | Debugging distributed systems | Full-stack operations teams | When to choose Honeycomb. * Your primary pain is debugging - finding why specific requests fail or slow down. * You run microservices and need to trace requests across service boundaries. * You already use other tools for infrastructure (Prometheus, Grafana) and logs (Loki, ELK). * You want OpenTelemetry-native tooling without vendor lock-in. * Your team is comfortable with a query-driven workflow (vs. dashboard-driven). When to choose Datadog. * You want one platform for infrastructure, APM, logs, and more. * Your team prefers dashboards and pre-built views over ad-hoc queries. * You need auto-instrumentation with minimal code changes. * You have the budget and want to minimize the number of vendors. * You need compliance features (SOC 2, HIPAA, audit logging) from a single vendor. When neither fits. Both Honeycomb and Datadog are built for teams running distributed systems at meaningful scale. But a large portion of modern applications do not look like that. If you are running a single Next.js application - deployed on Vercel or a VPS - you do not have distributed traces to analyze. You do not have 50 hosts to monitor. You have API routes that need to be fast, reliable, and monitored. For that scenario, both tools are overkill. Honeycomb's high-cardinality debugging is powerful but unnecessary when your "distributed system" is one application. Datadog's all-in-one platform costs more per month than most indie products earn. What you need is focused API monitoring: per-endpoint response times, error rates, status code tracking, and instant alerts when something breaks. Nurbak Watch is built for this exact use case. It runs inside your Next.js server via instrumentation.ts - five lines of code - and monitors every API route automatically. Alerts hit Slack, email, or WhatsApp in under 10 seconds. $29/month flat, free during beta. No per-host pricing, no per-span charges, no bill surprises. If your architecture grows into microservices, you can graduate to Honeycomb or Datadog. But start with what your architecture actually needs today. The Nurbak Team builds developer-first API monitoring tools. Nurbak share insights on uptime, performance, alerting, and best practices for keeping APIs healthy in production. Ready to try it? Nurbak Watch is free during beta. 5 lines of code. First alert in under 5 minutes. Comparisons

Deccan Herald
Mar 24th, 2026
AI security platform Datadog opens new office in Bengaluru.

AI security platform Datadog opens new office in Bengaluru. The new office strengthens Datadog's presence in a key growth market, enabling faster support, deeper local expertise, and scalable solutions for customers. Last Updated: 24 March 2026, 13:16 IST Follow Us: Credit: Reuters photo Bengaluru: Nasdaq-listed Datadog, an AI-powered observability and security platform for cloud applications, opened its new Bengaluru office at Embassy Golf Links Business Park on Tuesday. Anytime, anywhere. Experience a more refined e-paper today The new office strengthens Datadog's presence in a key growth market, enabling faster support, deeper local expertise, and scalable solutions for customers. Datadog has over 100 employees in India at present, serving over 400 organisations across commercial, midmarket, and enterprise segments. It supports many of the country's most-widely used digital platforms across food delivery, streaming and live sports, aviation, travel, fintech, e-commerce, and connected mobility. "The Bengaluru office is more than a footprint expansion - it's a strategic command centre for one of Datadog's fastest-growing markets," said Namit D' Cruz, Regional VP for India and SAARC at Datadog. "Local organisations have moved decisively from AI experimentation to full-scale production, and they're hitting a wall: fragmented tooling, blind spots, and security vulnerabilities that legacy solutions can't address." Stories You May Like "Our investment in R&D and AI is what sets Datadog apart. We are fully-resourced and ready to scale alongside our customers here, with local talent," said D' Cruz. Published 24 March 2026, 13:16 IST Follow us on:

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