• Build scalable Python-based data pipelines and backend services for analytics workflows. • Design software systems using object-oriented programming and sound engineering practices. • Create and support platforms that allow analytics development, model training, and model deployment. • Implement and maintain CI/CD pipelines and infrastructure-as-code solutions for automated deployments. • Manage cloud and on-premises analytics environments, including AWS infrastructure and security controls. • Monitor, troubleshoot, and improve data pipelines, platform performance, and system reliability. • Support machine learning and fraud modeling workflows, including feature engineering and model deployment. • Implement new technologies, including AI-based solutions, to improve platform efficiency and stability.

• Partner with business stakeholders across Operations, Customer Success, Product, Growth, and Learning to scope analyses, define metrics, and answer the questions that drive decisions. Translate ambiguous, business-language requests into clear analytical work that holds up to scrutiny and leads to action. • Drive the analysis itself — bring judgment about what question is actually being asked, what the right analytical approach is, and what "good" looks like. Push back when a request is underspecified and shape vague asks into rigorous analyses. • Own dashboards end-to-end with a strong business-intelligence mindset — design, build, maintain, and iterate based on stakeholder feedback to make data products that people actually use. You'll work in Hex (our primary BI tool), but we're looking for BI craft and judgment, not Hex-specific experience. • Adapt your message to the audience — surfacing the action-oriented takeaway for an executive, walking through the details with a peer, and producing written analyses that hold up after you've left the meeting. • Bring a data engineering and data architecture mindset — think about what data models should exist, how data flows between source systems and the warehouse, how schemas connect, and where pipelines need to be reinforced. Partner on data modeling decisions and contribute to the warehouse (in our case, primarily through dbt). • Take ownership of data quality and trust. Build tests into the models you work on, set up monitoring on the dashboards and pipelines that matter most, and respond when something breaks. The bar is "stakeholders should be able to trust what they see." • Collaborate closely with engineering — partner with the product and engineering teams on event instrumentation, source data quality, schema changes, and anything else upstream that affects what's available for analytics downstream. Be the analytics voice in engineering conversations, and the engineering-aware voice in analytics conversations. • Help shape the long-term direction of the data team — beyond day-to-day analysis, contribute your perspective on where the data architecture and analytics practice should be heading. Weigh in on what we should be measuring, how we should be instrumenting and collecting data, what tools belong in our stack, and where we should be investing for the next year and beyond. You won't own these decisions alone, but you'll be expected to bring informed opinions and help drive them forward. • Lean into an analytics culture where AI is already central to how we work. We use modern AI tooling (Claude Code, agentic skills) heavily in our day-to-day, and we're actively building out how the data team uses it. • Explore how AI-assisted workflows can improve the analytics craft - for SQL development, dbt modeling, dashboard prototyping, exploratory analysis, documentation, and code review. Bring practical experience and curiosity rather than buzzwords. • Contribute to how the data team develops reusable patterns, skills, and guardrails for working effectively alongside AI systems. • Help raise the bar across the small data team — review each other's SQL and dbt code, share patterns, and pair on tricky analyses. Be someone teammates want to learn from. • Bring others along in your work. Make decisions and trade-offs visible so the rest of the team learns from them, not just from the outcomes. • Operate as a steadying presence when priorities shift, requirements change, or analyses don't go the way anyone expected. Help the team stay focused on the decisions the work is meant to support.

• Assist in building and enhancing AI-powered applications and agents that support business workflows • Develop components of AI solutions that automate routine tasks and surface insights • Gather requirements from stakeholders with guidance from senior team members • Iterate on AI applications based on user feedback and testing results • Provide ongoing Level 3 support for software products • Support integration of AI applications with enterprise systems (e.g., EMR, HRIS, data platforms) under senior direction • Assist with deployment, testing, and monitoring of AI solutions in lower and production environments • Translate documented business requirements into functional workflows • Follow established standards for reliability, security, and code quality • Build and maintain ETL/ELT pipelines that feed analytics and AI use cases • Ingest and transform data from multiple source systems into centralized platforms • Validate accuracy, completeness, and structure of pipeline outputs • Develop and maintain semantic models, datasets, and dashboards (Power BI and related tools) • Apply standardized business metrics and KPI definitions across reports • Optimize queries and data structures for performance and usability • Implement and maintain row-level security and access controls on reports • Partner with IT, clinical informatics, operations, and business stakeholders to understand reporting and AI needs • Communicate progress, blockers, and trade-offs clearly to both technical and non-technical audiences • Escalate architectural or scope questions to senior engineers • Follow team practices for source control, code review, documentation, and testing • Monitor AI outputs and data pipelines for accuracy and reliability • Support compliance with data security, privacy, and governance standards (HIPAA-aware) • Build technical depth in AI/ML tooling, cloud services, and modern data platforms • Contribute small improvements to existing AI tools, dashboards, and pipelines • Stay current with emerging AI and analytics technologies relevant to healthcare operations

Role Description 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. - Work hand-in-glove with Data Engineering on what lands in the warehouse upstream. 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. - Stakeholder Partnership: Establish strong, durable working relationships with leaders and operators across Sales, Customer Success, and Revenue Operations. - Requirements Gathering: Sit with the people who use the data. Understand the operational reality behind a request before writing any SQL. - 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. - Modeling Craft: Author dbt models that are well-tested, well-documented, performant, and legible to the next engineer who picks them up. - Data Products & Tiering: Build to the right tier for the use case – from exploratory sandbox models for analyst-driven discovery to regulated, accuracy-critical models. - Commercial Acumen: Bring a strong intuition for how B2B SaaS revenue actually works. - Attention to Detail: Revenue numbers get scrutinised. Reconcile to source-of-truth systems, document edge cases, and surface assumptions. - Partner with Data Engineering: Work closely with the data engineering team on what lands in the warehouse from CRM, billing, product, and finance systems. Qualifications - 5+ years building and owning analytics engineering or modeling work. - Deep familiarity with B2B SaaS revenue schemas. - Strong, hands-on experience modeling data from HubSpot and/or Salesforce. - A demonstrated ability to take loose, ambiguous, or evolving requirements and turn them into well-structured dbt models. - Ability to ship a sharp answer quickly when speed matters. - Strong commercial instincts. - Genuinely meticulous with attention to detail. - Strong working knowledge of modern data infrastructure. - Clear written and verbal communication skills. - This role is based in Australia. What will give you an edge? - Time spent in a high-growth tech business. - Hands-on experience working alongside Revenue Operations. - Exposure to forecasting workflows or conversational analytics surfaces. - Experience building models that bridge from Revenue analytics into FP&A. - Familiarity with adjacent source systems. - Opportunities to provide support for the broader organisation, especially our Marketing function. Benefits - Monthly Lifestyle subsidy for financial, physical, and mental well-being. - WFH set-up allowance to ensure you have the right environment to work in. - $1500 USD annual Learning & Development allowance. - Annual leave: PTO plus two additional UpGuardian leave days. - 18 weeks paid Parental Leave irrespective of parenting role. - Personal Leave Allowance including sick & carer’s leave. - Fully remote working environment. - Top-spec hardware provided for all team members. - Generative AI subsidy for access to generative AI tools.