Head of Data & Analytics

VTS•New York, NY

1d•$190,000 - $250,000•Onsite

About The Position

The Head of Data & Analytics is a high-visibility, foundational role responsible for establishing the company's single source of truth for data across all functions. Acting as the analytical backbone of the business, you will ensure every critical metric is defined, governed, and reportable from one place — giving leadership the data clarity they need to make fast, confident decisions. You will translate raw data across a modern SaaS stack into a trustworthy, scalable intelligence layer that the entire organization can rely on. This role is designed for a seasoned data leader who excels at building from scratch — someone who can architect a data platform, establish governance standards, and be a genuine strategic partner to senior leaders – not just delivering data, but shaping decisions. You will connect data to business context, identify what matters, and provide clear, prescriptive recommendations. Reporting directly to the SVP of Business Operations, you will own the data infrastructure that underpins our next phase of growth. This role is intentionally hands-on to start. You will personally build and shape the foundation of our data platform while partnering closely with leaders across the business. As we scale, you will have the opportunity to evolve into a player-coach, building and leading a small but high-impact data team. In the first 6-12 months, you will: Establish a clear, trusted definition of core company metrics across GTM, Product, Finance, and Customer Success Reduce reliance on manual reporting by automating high-impact workflows Build a scalable data model that connects our core systems into a coherent source of truth Enable leadership to access and trust data without needing constant ad hoc support

Requirements

7-10+ years in analytics, analytics engineering, or a senior BI role with hands-on data modeling experience, including experience owning or significantly shaping a company’s data model or analytics infrastructure
Experience building and governing a BI layer (e.g., Looker, Tableau): you know what good looks like, what breaks at scale, and how to make a BI tool something people actually use.
Experience working with modern data warehouses (e.g., Snowflake, BigQuery, Redshift): Warehouse management, schema design, cost monitoring — you understand the platform and can make architectural decisions on it.
dbt fluency: You can read, write, review, and govern dbt models. You understand staging, intermediate, and mart layer design, and you have strong opinions about what belongs where and why.
Strong SQL: Window functions, CTEs, and performance optimization are comfortable ground. You write modeling SQL, not just analytical SQL.
Broad business fluency and SaaS literacy: You understand the operational levers of a SaaS P&L - ARR, NRR, churn, CAC, LTV, product velocity, product adoption, as well as financial levers, such as unit economics, financial performance ratios, financial analysis.
Track record of strategic partnership: You have been in rooms with senior leaders and said things that changed decisions — not because you were asked, but because the data told you something important. You elevate data from reporting to a strategic asset that shapes how the company operates and makes decisions.
Comfort operating without formal authority: You can direct an analytics engineer you don't manage, get a VP to prioritize data quality, and get Finance to adopt a metric definition they initially resisted.
Strong prioritization instincts: You focus on the highest-impact problems and avoid over-engineering, balancing speed with long-term scalability.

Nice To Haves

Experience building from scratch: You have built a data function, a data layer, or a centralized reporting infrastructure at a company that previously had none. You know how to sequence the work and how to manage stakeholder expectations during the build phase.
Salesforce data model familiarity: You understand how CRM objects map to business concepts and how to extract Salesforce data reliably into a warehouse environment.
Product analytics experience: You have worked with Mixpanel or a comparable tool and know how to join event-level product data with CRM and financial data to create a complete picture of customer behavior.
C-suite and investor exposure: You know what executives and investors ask for and what breaks under that scrutiny.
CS analytics background: Experience with customer health scoring, adoption reporting, proactive value engineering, or churn prediction data models, is a meaningful plus.
Background in FP&A, investment banking, or management consulting before moving into data — brings structured analytical thinking and executive communication skills.

Responsibilities

Build the single source of truth: Audit, rationalize, and own the company's data model end to end — from raw sources through transformation to consumption. Every key metric will have one definition, one owner, and one place to find it.
Identify the highest-impact manual reporting workflows across functions and automate them: Today, much of our reporting lives across spreadsheets and manual workflows, with no single owner of data. One of your first priorities will be to bring structure, consistency, and trust to this environment, and build a self-serve analytics layer that increases data trust and reduces ad hoc requests across the company.
Own executive and board-level reporting: Build and maintain the metrics layer that leadership, the board, and ultimately investors rely on — each defined, documented, and reportable from a single governed layer.
Mature the dbt layer: Partner with engineering team to audit existing dbt models, close gaps, and establish transformation standards that will scale the platform. You won't be coding alone, but you need to be fluent enough to review, direct, and govern the work.
Establish metric governance: Define the operating model for how metrics are created, changed, and communicated — including a cross-functional process for resolving metric disputes and maintaining a company-wide data dictionary that becomes the authoritative reference.
Build a scalable, auditable data foundation: Ensure our data is structured, documented, and fully traceable from source systems to reported metrics. Establish clear data lineage, metric definitions, and governance so leadership can rely on data with confidence as reporting needs become more complex.
Act as a strategic partner to senior leadership: Show up to QBRs, pipeline reviews, and planning cycles with a clear point of view on what the data means and what the business should do next. Be the person who changes decisions — not by reporting the data, but by interpreting it and making actionable recommendations.
Cross-functional data leadership: Partner with all functions to ensure their data needs are captured, while also challenging assumptions, refining questions, and guiding teams toward the metrics and analyses that drive better decisions.
Drive insight and action, not just visibility: Go beyond building dashboards to proactively identify trends, risks, and opportunities across the business. Translate data into clear recommendations and influence leaders on what actions to take — even when it challenges existing assumptions or priorities.