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Analytics EngineerUpGuard • United States
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Analytics Engineer

UpGuard
  • US
    United States
  • US
    United States
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À propos

Analytics Engineer
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 documentation across the revenue domain. Data Products & Tiering: Build to the right tier for the use case – from exploratory sandbox models for analyst-driven discovery, through self-serve modeled marts, up to the regulated, accuracy-critical models that feed Finance, board reporting, and the executive team. Be opinionated about what belongs where. Commercial Acumen: Bring a strong intuition for how B2B SaaS revenue actually works – the relationships between bookings, billings, ARR, recognised revenue, pipeline coverage, conversion, sales cycle, and retention – and let that intuition shape the models you build, not just validate them after the fact. Attention to Detail: Revenue numbers get scrutinised. Reconcile to source-of-truth systems, document edge cases, surface assumptions, and make sure the number in the board deck is the same number in a dashboard. Sweat the small stuff because the executive team will. Partner with Data Engineering: Work closely with the data engineering team on what lands in the warehouse from CRM, billing, product, and finance systems. Be a clear, specific, respectful customer of sourcing – the kind of partner that makes the upstream team's job easier, not harder. What will you bring? Proven Craft: 5+ years building and owning analytics engineering or modeling work, with significant time spent on Revenue, Sales, or Customer Success domains within a B2B SaaS company. Revenue Domain Expertise: Deep familiarity with B2B SaaS revenue schemas – ARR, bookings, pipeline, opportunity lifecycle, churn, expansion, contraction, NRR, GRR – and the practical messiness of how these metrics are calculated and reconciled in a real business. CRM Experience: Strong, hands-on experience modeling data from HubSpot and/or Salesforce. You know where the bodies are buried – object relationships, history tables, the difference between what the CRM says and what reality is – and you can navigate it. Requirements Translation: A demonstrated ability to take loose, ambiguous, or evolving requirements and turn them into well-structured dbt models, clear metric definitions, and outputs stakeholders can act on without needing to ask follow-up questions. 80/20 Judgement – and the Discipline to Build Properly: You can ship a sharp answer quickly when speed matters, and you can build a durable, governed data product when the situation demands it. You know which one you're doing and why, and you can defend the call. Commercial Acumen: Strong commercial instincts – you understand why the business cares about a metric before you build it, and your models reflect commercial reality, not just schema convenience. Attention to Detail: Genuinely meticulous. You reconcile, you test, you document, you check the edges. You don't ship a revenue model and find out it's wrong from the CFO. Data Infrastructure Expertise: Strong working knowledge of modern data infrastructure – BigQuery, dbt, modern data sourcing tools (Fivetran, AirByte, or similar), and a modern BI layer (Looker, Omni, or similar). Communication & Influence: Clear written and verbal communication. You can explain a modeling decision to a CS Director, a metric definition to the CFO, and a backfill plan to a data engineer – and each one walks away aligned. Australia-based: This role is based in Australia. We work in a fully remote setup with strong overlap across our Sydney and Hobart hubs. What will give you an edge? High-Growth SaaS Experience: Time spent in a high-growth tech business, especially through the scale-up phase where the data layer has to evolve underneath a moving business. RevOps Fluency: Hands-on experience working alongside Revenue Operations – territory and quota modeling, pipeline hygiene, forecast inputs, comp data. Forecasting & Conversational Analytics: Exposure to forecasting workflows or conversational analytics surfaces, and a view on how the modeled layer needs to support them. Finance Adjacency: Experience building models that bridge from Revenue analytics into FP&A – the seams where ARR meets recognised revenue, where pipeline meets plan. Lifecycle Across the Stack: Familiarity with adjacent source systems – billing platforms, product usage data, customer success tooling – and how their data needs to slot into a coherent revenue layer. Marketing Experience: While primarily focused on Revenue, in time there will be opportunities to provide support for the broader organisation, especially our Marketing function. What's in it for you? Monthly Lifestyle subsidy: Use this for financial, physical, and mental well-being WFH set-up allowance: To ensure you have the right environment to work in, we will help you get set up within your first 3 months at UpGuard $1500 USD annual Learning & Development allowance: To support your career development, all team members will be able to expense development opportunities against this allowance Annual leave: PTO plus two additional UpGuardian leave days to give you time to recharge your batteries. 18 weeks paid Parental Leave: Irrespective of parenting role Personal Leave Allowance: This includes sick & carer's leave Fully remote working environment: While we have physical offices in Sydney & Hobart, we do not mandate compulsory attendance Top-spec hardware: All team members will be provided with top-spec laptops for their role Generative AI subsidy: UpGuard provides paid subscriptions for all team members to access generative AI tools to support their work
  • United States

Compétences linguistiques

  • English
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