Datadog is looking for a Partner Solutions Architect (EMEA).
Datadog is, without exaggeration, one of the titans in the world of observability, monitoring, and security. If you have cloud infrastructure, applications, microservices — chances are you either already use Datadog or have at least considered it. The company is so deeply entrenched in data and analytics that even its job description mentions the “AI era.” Ironic, isn’t it?
So what problem are they trying to solve by posting this vacancy? They are looking for someone who will be a Swiss Army knife for their partners in the EMEA region. Partners are system integrators, MSPs, consultants who sell and implement Datadog solutions for their clients. The company’s pain point is obvious: how to scale expertise? How to ensure that every partner is as well-trained as an internal employee, understands all the nuances of the product, can deliver a brilliant demo, and correctly conveys value to the client? How to collect feedback from hundreds of partners and turn it into meaningful requests for the development team? For this, they need a bridge-person, a mentor-person, a translator-person from partner-speak to engineering-speak.
Now, let’s imagine for a second that we are solving this problem not by hiring another “senior,” but by creating a system. Let’s call it, say, “Digital Partner Solutions Architect.”
— Petrovich, did you see the Datadog vacancy? They’re looking for someone to train partners, hold their hand, help them prepare demos, listen to their pain points, and lobby internally. A classic role, requires a very experienced and communicative techie.
— I saw it, Oleg. And you know what I thought? They, a company from the AI era, are trying to scale knowledge in the most inefficient way — through people. One person, even the most brilliant, has a limit on time and attention. They can conduct three meetings a day, not three hundred.
Let’s break down how we would build this “Digital Architect.”
The foundation will be a private, fine-tuned large language model (LLM). As a base, we could use Llama 3, Claude 3, or even an API from OpenAI. We will feed this model exclusively with internal Datadog data: all technical documentation, knowledge base, webinar recordings, successful case studies, internal “best practices” guidelines, and, most valuable of all, — anonymized ticket history from customer support.
How will this work in practice?
1. Onboarding & Enablement. A new technical specialist from a partner logs into the portal and writes: “Our company specializes in migrating fintech applications from on-premise to AWS. Where should we start learning Datadog?” Instead of waiting for a call from a live architect, the system instantly generates a personalized training plan: “Excellent! Here are 3 key modules for you: AWS Lambda Monitoring, APM for Java Applications, and Security Monitoring for PCI DSS compliance. Here are links to documentation, two relevant webinars, and a simulator to prepare for certification.” The system tracks progress and provides new materials.
2. Pre-sales support. A partner is preparing for a meeting with a potential client. They enter brief data into the system: “Client is e-commerce, uses Kubernetes, complains about slow page loading during peak hours.” The “Digital Architect” responds: “I recommend focusing on Real User Monitoring (RUM) and APM. Here’s a customized script for a demo. Here are three case studies of similar clients. And here’s a calculation of potential ROI for the client by reducing response time by 200ms.”
3. Partner Advocacy. Instead of a live person listening to complaints and requests, the system connects to partner Slack channels, analyzes email correspondence, and Jira tickets. Using NLP and sentiment analysis, it generates a real-time dashboard for Datadog product managers: “Attention: over the last month, partners from Germany requested improved SAP integration 17 times. Partners from France encountered problem X 23 times when configuring log monitoring.” This is not just feedback collection; it’s data-driven analytics.
4. Proactive consulting. The system periodically scans public partner websites and their marketing materials. It can send a notification: “Noticed you announced a new ‘DevOps transformation’ service. You don’t mention our CI/CD Visibility capabilities in it. I recommend adding this to your offering. Here’s a draft paragraph for your website and an email for your clients.”
To ensure partners and internal teams don’t fear this “black box,” it needs to be implemented wisely. Initially, it works as an assistant for a live architect, helping them prepare materials. Every AI recommendation should be accompanied by links to sources — a specific document, case study, or webinar. There should be a “report an issue” or “rate usefulness” button so the system continuously learns.
And how do we verify that our “Digital Architect” is working? Metrics are everything.
1. Average time for onboarding and certifying a new partner. Should decrease by 30-50%.
2. Number of standard technical questions reaching live people. Should drop significantly.
3. Speed of the product team’s reaction to partner requests. With automated data collection, it will become almost instantaneous.
4. And, of course, the main indicator — revenue growth through the partner channel. An A/B test can be conducted: give one group of partners access to the AI assistant, and not the other. I am confident the results will be very telling.
So yes, Datadog can hire another excellent specialist. Or it can become an example for the entire industry and create a system that does the same thing, but a thousand times faster and at a greater scale. The only question is whether they are ready to apply their own philosophy of data and automation to their internal processes.
Источник: https://www.linkedin.com/jobs/view/4382121277/
