Analytics Engineer

WHO ARE WE?
At UpGuard, we are replacing manual security bottlenecks with AI-driven precision. Fresh off a US$75M Series C, we are scaling our infrastructure to process 100 billion risk signals daily. This isn’t just growth; it’s a total reimagining of how the world manages cyber risk.

We build the Cyber Risk Posture Management (CRPM) platform that security teams actually love. By integrating security ratings, threat intel, and agentic AI, we empower organisations to stay ahead of an ever evolving attack surface.

We aren’t just building another tool; we’re defining a category. We provide the autonomy to ship world-class technology and the resources to do it at a global scale.

At UpGuard, our Sales team continues to be the engine of our growth, moving beyond simple transactions to become trusted architects of digital resilience. In an era where third-party risk is more complex than ever, we maintain a highly collaborative, consultative culture that puts the customer’s security posture above all else.

Following a record-breaking 2025 where we shattered revenue targets and expanded our global footprint, we are entering 2026 with even greater momentum. We are seeking high-energy individuals who thrive in a fast-paced, AI-augmented sales environment. If you are hungry to sharpen your craft, possess a relentless desire to win, and want to sell a platform that is actively neutralising global cyber threats, we want to talk to you.
Who are we?
At UpGuard, we are replacing manual security bottlenecks with AI-driven precision. Fresh off a US$75M Series C, we are scaling our infrastructure to process 100 billion risk signals daily. This isn’t just growth; it’s a total reimagining of how the world manages cyber risk.

We build the Cyber Risk Posture Management (CRPM) platform that security teams actually love. By integrating security ratings, threat intel, and agentic AI, we empower organisations to stay ahead of an ever evolving attack surface.

We aren’t just building another tool; we’re defining a category. We provide the autonomy to ship world-class technology and the resources to do it at a global scale.

Our Analytics team at UpGuard supports all of our teams to extract key insights from the data that we collect. We extract data from multiple sources and build insightful dashboards that display key metrics and support leaders in decision-making. We also build integrations between systems to collect required data seamlessly.

WHERE DOES THIS ROLE FIT IN?
As UpGuard continues its rapid growth trajectory, we are investing in an Analytics Engineer to architect the foundational data layer that powers our entire growth engine. Reporting to the Director of Analytics, this role is the critical bridge between complex commercial strategy and technical execution. You will completely own the modeled revenue data layer end-to-end, serving as the ultimate authority on the data models that Sales, Customer Success, Finance, and the executive team rely on to make enterprise-level decisions.
Operating as a senior individual contributor and the first Analytics Engineer, you will own the modeled revenue layer, partner closely with our Revenue Analytics Lead on how it's used to drive insight, and work hand-in-glove with Data Engineering on what lands in the warehouse upstream. Together, we will accelerate our ability to ship high-impact data products and establish the paradigms for how analytics engineering is practiced at UpGuard.
What will you do?
-
– Revenue Data Layer Ownership: Design, build, and maintain the foundational revenue data models in dbt – ARR, pipeline, bookings, churn, expansion, retention, attach rates, and the data models that join them to customer, opportunity, and product context. This is your domain end-to-end.
– Stakeholder Partnership: Establish strong, durable working relationships with leaders and operators across Sales, Customer Success, and Revenue Operations. Translate loose, evolving requirements into well-specified models and clear metric definitions that stakeholders trust and can actually use.
– Requirements Gathering: Sit with the people who use the data. Understand the operational reality behind a request before writing any SQL. Distinguish the actual decision being made from the artefact that was asked for, and bring back a sharper proposal than the one you were handed.
– Pragmatic Delivery: Be excellent at 80/20-ing. Ship the version that unlocks the decision this week, while building the foundations that hold up next quarter. Know when a quick dbt model and a clear caveat is the right call, and when the situation calls for a proper, governed, tier-one data product. Make that judgement explicit, not implicit.
– Modeling Craft: Author dbt models that are well-tested, well-documented, performant, and legible to the next engineer who picks them up. Establish and uphold conventions for naming, layering (staging → intermediate → marts), testing, and docu