butterflymx - VP of Data

• The strongest candidates will demonstrate the following qualities. • VISIONARY AND TRANSFORMATIONAL • You have a clear point of view about what a world-class data organization should become. • You can translate that vision into an executable sequence of systems, products, operating changes, and organizational decisions. • You have led meaningful transformation rather than merely maintaining a mature environment. • You can create momentum while building foundations that will endure. • You know how to distinguish transformative priorities from attractive distractions. • COMMERCIALLY FLUENT • You speak the language of the business, including ARR, bookings, retention, expansion, churn, pipeline, conversion, payback, margin, productivity, and unit economics. • You understand how SaaS businesses create and lose enterprise value. • You can connect data investments to revenue, cost, customer outcomes, risk, and organizational effectiveness. • You can challenge a business assumption as credibly as you can challenge a data model. • You are interested in decisions and economics, not just SQL and dashboards. • TECHNICALLY CREDIBLE • You possess deep knowledge of modern data architecture, engineering, analytics engineering, governance, quality, and business intelligence. • You can challenge architecture decisions and identify weak technical reasoning. • You remain capable of writing code, reviewing models, debugging pipelines, and prototyping solutions. • You are senior enough to set strategy but have not become detached from implementation. • You understand when technical simplicity is strength and when additional rigor is required. • OPERATIONALLY GROUNDED • You have built or transformed a data function at a company with comparable scale, complexity, and growth dynamics. • Your experience is not limited to either an early-stage startup with little operating complexity or a large enterprise with abundant specialization and resources. • You know how to sequence investments when the company needs better answers immediately but foundational work is incomplete. • You have operated through ambiguity, imperfect systems, limited capacity, and competing priorities. • You build processes that are disciplined without being cumbersome. • You use AI agents as a normal part of your own daily work. • You have personally built and shipped at least one production agent that created meaningful value. • You understand agent architecture, tool use, context, evaluation, observability, permissions, failure modes, and human oversight. • You can identify workflows that are genuinely improved by agents and reject those that are not. • You know how to turn experimental prototypes into dependable operational capabilities. • You treat datasets, metrics, semantic models, dashboards, and agent interfaces as products. • You care about users, adoption, discoverability, usability, trust, support, versioning, and deprecation. • You understand that shipping a dashboard is not the same as changing a decision. • You simplify the experience of using data rather than transferring technical complexity to consumers. • You measure whether the product achieved its intended business outcome. • POLITICALLY MATURE • You can navigate disagreement among functions without becoming a weapon for any one group. • You understand that metric disputes often reflect real differences in incentives, workflows, definitions, and decision needs. • You listen carefully, identify the underlying issue, and facilitate principled resolution. • You build relationships without sacrificing independence. • You can tell an executive that their preferred interpretation is unsupported while preserving trust and forward progress. • You know when to compromise on implementation and when not to compromise on truth. • TRUTH-SEEKING AND PRINCIPLED • You are willing to disagree with the room when the evidence disagrees. • You distinguish confidence from certainty and make assumptions explicit. • You do not manipulate definitions or analyses to produce a desired answer. • You communicate inconvenient findings diplomatically but directly. • You are relentless about getting to the truth while remaining open to being wrong. • You demonstrate sound judgment, integrity, and intellectual honesty even when doing so is difficult. • HEADSTRONG AND COLLABORATIVE • You can build broad support for difficult changes. • You are empathetic toward legitimate stakeholder constraints without allowing them to become permanent excuses. • You know how to push hard without becoming needlessly adversarial. • You can absorb disagreement, remain composed, and continue driving toward the right outcome. • You create clarity, establish ownership, and follow through until change is implemented. • 12+ years of progressive experience across data engineering, analytics engineering, business intelligence, data architecture, data products, or related disciplines. • 5+ years leading professional data teams, including experience managing managers or senior technical leaders. • Demonstrated success building or transforming a data function within a growing SaaS or technology company of comparable scale. • Deep experience with modern cloud data platforms, preferably including Snowflake. • Strong experience with data transformation and modeling frameworks such as dbt. • Experience with managed ingestion platforms such as Fivetran and with designing reliable source-to-warehouse pipelines. • Strong experience with business intelligence and self-service analytics platforms such as Sigma. • Deep understanding of dimensional modeling, semantic layers, entity resolution, master data, golden records, data contracts, lineage, observability, and quality engineering. • Experience integrating and governing data from Salesforce and other core SaaS business systems. • Demonstrated ability to establish governed business metrics and resolve cross-functional definition disputes. • Strong commercial understanding of recurring-revenue business models and SaaS operating metrics. • Experience designing data products for executive, operational, analytical, and machine consumers. • Experience supporting planning, forecasting, customer lifecycle analysis, go-to-market analytics, financial reporting, and product analytics. • Personally built and shipped at least one production AI agent or agentic workflow. • Strong understanding of AI-agent evaluation, tool access, context management, observability, permissions, security, and operational reliability. • Hands-on technical capability to write and review SQL, inspect dbt projects, evaluate models, troubleshoot pipelines, and prototype solutions. • Exceptional written and verbal communication. • Demonstrated ability to influence senior executives and drive difficult cross-functional changes. • Bachelor’s or advanced degree in a relevant technical, quantitative, or business field is welcome but not required. Exceptional experience, judgment, and results matter more than credentials. • HOW WE WILL EVALUATE CANDIDATES • Strong candidates should be prepared to discuss specific examples of: • A data transformation they personally led, including the starting condition, resistance encountered, architecture chosen, sequence of work, and measurable business results. • A source-of-truth or golden-record initiative they implemented across conflicting systems. • A metric-definition dispute involving multiple executives or functions and how they resolved it. • A time the data contradicted the prevailing organizational narrative and how they handled it. • A data product that materially changed decisions or operating behavior. • Code, models, analyses, or technical prototypes they personally produced while operating in a senior leadership role. • A production AI agent they personally built, how it worked, how it was evaluated, and what measurable value it created. • A time they had to push a highly resistant stakeholder or function to adopt a necessary change. • A failure or data incident for which they accepted accountability and the systemic changes that followed. • A difficult hiring, performance, or organizational decision they made to raise the quality of a data team.