About the role Our data function is being built from scratch. We are looking for the person who will stand up Jazz’s data platform end to end – ingestion, modeling, warehouse, orchestration, cost control – and then turn it into the numbers the company actually runs on: the executive metric set, the GTM and pipeline view, and delivery reporting for R&D. Owning the platform is a huge part of this role, but success is about more than just keeping pipelines green. You’ll partner directly with execs, R&D, and sales to agree on core metric definitions, then make sure everyone across the company can easily get the data they need on their own. What you’ll do Build the platform from zero. Design and implement the databases, ELT/ETL pipelines and orchestration on a medallion architecture that other people can safely build on top of. Own the gold layer as the single source of truth. Modeled, tested, documented and trusted, the layer the company argues from, not about. Define the company’s metrics. Work with leadership to define organizational KPIs, instrument them, and keep the definitions honest as the business changes. Build dashboards that executives actually use. Take complex, multi-source data and make it legible to a C-level reader in thirty seconds. Serve every BI consumer at Jazz. Sales and GTM (pipeline, funnel, forecast, Salesforce data), R&D (delivery, quality, product usage), Finance and Operations. Run it in production, at scale. Reliability, data quality, alerting and monitoring, including active tracking and reduction of infrastructure and warehouse costs. Set the standards. Modeling conventions, testing, documentation, access control, and how BI requests get intake and prioritized. Make things happen across the org. Present, persuade, and drive alignment between stakeholders who define the same metric three different ways. Work with AI as a multiplier. We expect heavy, deliberate use of AI in how you build, model, investigate and enable others. What you need to have 5+ years in BI, analytics engineering, or data engineering, including at least one role where you built the data stack from scratch and grew it as the company grew. Experience at a company with large and complex data volumes. Expert-level SQL and Python, plus hands-on Spark in production. Proven experience designing and building a medallion-architecture warehouse, when you shaped the layers, not just wrote models inside someone else’s design. DBT and Databricks (or an equivalent lakehouse + transformation framework) in production. A track record of dashboards used by C-level stakeholders. You can point to a specific executive view you built and the decisions it changed. Experience defining organizational metrics and securing cross-functional agreement on them. Hands-on experience with Salesforce data – the object model, its quirks, and how to model it reliably. Ownership of production data infrastructure at scale, including cost monitoring and optimization. Strong presenter and communicator. You can hold a room of executives, bring a debate to a decision, and bridge between stakeholders who disagree. Flue
