Senior Data Analyst

Wagmo•New York, NY

56d•Hybrid

About The Position

We are hiring a Senior Data Analyst to turn data into decisions that accelerate revenue, margin, and operational efficiency. Reporting to the VP of Finance, you’ll own the data foundation, analytics and reporting across the funnel—partnering with Finance, Sales, Marketing, Product, and Operations—to define metrics that matter, build trusted reporting, and surface insights that shape strategy. You’ll combine hands-on SQL/dbt chops with strong business intuition to drive the roadmap for data-driven growth.

Requirements

3+ years in data analytics, business intelligence, or data engineering (ideally in high-growth SaaS/consumer tech), with a track record of owning SQL/dbt pipelines, building executive-ready dashboards, and turning business questions into decision-grade insights.
Advanced SQL; modeling with dbt on BigQuery (or similar); Git workflows; strong visualization instincts (Tableau or analogous BI).
Proven track record of working autonomously with strong organizational, prioritization, and time management skills. Preference for experience in start-ups or fast-growing, scaling companies.
Clear, direct communicator able to frame trade-offs, persuade with data, and align cross-functional stakeholders. Experience partnering with Customer Success, Sales & Marketing, Operations, Product/Engineering, Finance.
Fluency in SaaS metrics (LTV/CAC, payback, retention/cohorting, pipeline), experimentation, and unit economics.
Track record turning ambiguous questions into clear metrics, analyses, and decision-ready recommendations that move KPIs.
Organized, autonomous, and comfortable operating in a fast-moving, high-ownership environment.

Responsibilities

Technical data modeling & analytics engineering: Translate ambiguous questions into precise logic and performant SQL; design and maintain robust, explorable dbt models/semantic layers. Own end-to-end build in dbt/SQL/Git with tests (schema/uniqueness/RI), CI/CD, code reviews, and reliable backfills. Deliver production-grade marts and dashboards that are accurate, maintainable, and easy to self-serve.
Build executive-ready reporting. Automate self-serve dashboards and scheduled reports for weekly business reviews, monthly close, OKR reviews, and board materials; drive adoption and data literacy across teams. These reports should provide actionable insights and track key performance indicators to support strategic planning and operational efficiency across the departments and functional areas.
Lead growth & revenue analytics. Analyze the end-to-end funnel; quantify drivers of acquisition, conversion, retention, and expansion; size opportunities and recommend actions with clear impact estimates. Pro-actively surface insights to stakeholders to drive data usage and informed decision-making by regularly reviewing data trends, identifying opportunities for improvement, and providing recommendations.
Tell the story. Communicate complex findings clearly to technical and non-technical audiences; tailor visuals and narratives to drive alignment and decisions.
Operationalize insights. Embed metrics & alerts into workflows (Sales, Marketing, Operations, CS) and create playbooks so teams act on signals quickly (e.g., churn risk, lead quality, customer support and claims processing insights, margin anomalies).
Elevate data quality & governance. Establish SLAs for freshness/accuracy, anomaly detection, and issue triage; maintain a business glossary and dashboard QA standards; align with privacy/compliance guidelines. Document findings and definitions to improve company-wide understanding by creating comprehensive documentation for data processes, methodologies, and terminologies.