• 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.

• Design and build the reporting data architecture, structuring BigQuery datasets, tables, and views for scalable, multi-account, and multi-venue analytics. • Build and manage ingestion pipelines from multiple disparate data sources, utilizing APIs, webhooks, scheduled syncs, ETL/ELT tools, or custom scripts. • Set up and manage data exports from GA4 to BigQuery, establishing clean data relationships across all reporting entities including accounts, venues, campaigns, and subscription tiers. • Integrate diverse data streams including Google Tag Manager, CMS platforms, email and SMS marketing tools, receipt validation services, and POS systems. • Create clean SQL models to transform raw behavioral data and business metadata into reusable, reporting-ready tables. • Prepare foundational data models to power automated dashboards and client-facing analytics for various BI platforms like Looker Studio. • Establish comprehensive data quality assurance processes, validation checks, and actively monitor pipeline reliability. • Maintain clear, thorough documentation for schemas, data flows, transformations, reporting logic, and event taxonomies. • Partner cross-functionally with product and engineering teams to ensure tracking is implemented correctly and to support privacy-conscious data practices.

• Own the entire lifecycle of data delivery- architecting pipelines from raw sources through dbt models and semantic layers into final Looker dashboards. • Embed directly with stakeholders to scope complex business questions, execute rigorous analyses, and independently translate ambiguous requirements into clear, decision-ready answers.

• We are hiring a Senior Finance Analytics Engineer on the Finance Data Team to support data modeling, reporting, and designing data products for an AI-native consumption model. • Design and build dbt models that serve as the source of truth for Finance & Accounting reporting, planning, and analysis. • Partner directly with stakeholders in Finance, Accounting, Revenue, and FP&A to define metrics, shape requirements, and translate business logic into well-structured models. • Write and optimize complex SQL against our Databricks environment with attention to accuracy. • Uphold and evolve patterns for model design, testing, versioning, and data contracts across the dbt project. • Design the semantic layer and metrics definitions (MetricFlow, Cube, or equivalent) that both humans and agents query against. • Drive the documentation flywheel across dbt, Databricks, Confluence, and the semantic layer so that models, columns, and metrics are legible to LLMs and analysts alike. • Use AI tooling (Claude Code, Cursor, and our internal capabilities) as a daily part of your own development workflow.