Director, Internal Analysis

Responsibilities

• Lead, coach, and grow a high-performing team of Analytics Engineers and Data Scientists. Invest in their development and maintain a high bar for technical craft.
• Shape data culture by establishing clear KPIs, helping teams define success upfront, and embedding analytics as a strategic partner from the start of initiatives.
• Own the full data lifecycle: guide ingestion, modeling through medallion architecture on BigQuery, and production of trusted tables and reporting artifacts as the company's single source of truth.
• Apply AI capabilities such as anomaly detection, forecasting, or AI-assisted insight generation to strengthen both internal operations and the product.
• Partner with leaders across Product, Engineering, Marketing, Sales, CS, and Finance to surface key questions, translate them into analytical approaches, and deliver actionable recommendations.
• Raise the technical bar for pipeline reliability, SQL, dbt practices, data modeling, experimentation, and statistical rigor.
• Translate company strategy into team priorities and guide the team through evolving business needs.
• Innovate by combining warehouse data, product analytics, and experimentation tools; leverage AI for coding, pipeline development, and analysis to focus team time on judgment and insight.

Requirements

• Progressive experience in data and analytics in a B2B SaaS environment, including 3+ years of people management across Analytics Engineering and Data Science (more senior candidates with centralized data function leadership encouraged).
• Highly effective at driving organizational change, building trust with cross-functional leaders, influencing goal-setting and measurement, and embedding data into decision-making.
• Strong technical fluency across the full data stack: ingestion and pipeline orchestration, SQL, dbt, data warehouse design (BigQuery, Snowflake or similar), Python, statistical modeling, experimentation, forecasting, and AI application.
• Fluency applying AI both to increase team velocity (tools for coding/analysis) and to evaluate AI-driven product capabilities (forecasting, anomaly detection, automated insights).
• Deep understanding of Data Science craft to coach on experimentation design, causal inference, and appropriate method selection.
• Track record of partnering with Product and GTM leaders where analytical work materially changed company operations.
• "Player-coach" mentality: comfortable with hands-on pipeline, model, or SQL work while leading through others.
• Clear communication of complex concepts to both technical and non-technical stakeholders, including senior leaders.
• Natural mentor with history of building inclusive, high-achieving teams.
• Direct experience with modern data stack (BigQuery, dbt, Fivetran, Hex).

Nice-to-Haves

• Experience with Mixpanel or other product analytics tools (Amplitude, Heap, Pendo).
• Familiarity with experimentation platforms and operational aspects of healthy A/B testing programs.
• Background in PLG and/or hybrid go-to-market motions.
• Openness to being a public-facing voice for analytics and product analytics (speaking, writing, shaping best practices in SaaS community).