Director of Data

Core Responsibilities

• Lead, mentor, and grow a small but mighty data team while remaining hands-on with data architecture, modeling, technical direction, and analytics.
• Contribute directly as an individual contributor, particularly in data engineering, data infrastructure, and analytics engineering, helping establish high standards for data quality, reliability, and usability.
• Lead data strategy and execution across Outschool’s core data systems, including Redshift, dbt, and Opensearch, and self-serve tools such as Omni, Amplitude, and Statsig.
• Partner cross-functionally with Product, Engineering, Marketing, Operations, Finance, and other teams to deliver trusted, decision-ready data resources and analytics.
• Collaborate with senior leadership to align on strategy, priorities, metrics, experimentation practices, and long-term roadmap planning.
• Foster a culture of data quality, ownership, documentation, self-service, experimentation, and continuous improvement through mentorship and feedback.
• Lead adoption of self-serve data practices across the organization, ensuring teams can access and use data effectively without requiring significant centralized support.
• Help define and implement modern data practices that improve speed, trust, discoverability, and leverage across analytics, experimentation, and applied AI / ML.

Desired Experience & Skills

• 10+ years of experience in data engineering, analytics engineering, analytics, data science, ML, software engineering, or related technical roles.
• 5+ years of management experience leading data, analytics, engineering, or ML teams, with demonstrated proficiency in performance management, career development, and people logistics.
• Strong experience with modern data infrastructure and analytics engineering, including SQL, dbt, data modeling, pipeline reliability, and warehouse architecture, as well as building self-serve analytics systems and practices using tools such as Omni, Looker, Amplitude, Statsig, Mode, Hex, or similar platforms.
• Experience with deep analytics / data science, including influencing stakeholders with data-informed decisions and strategies.
• Experience balancing data leadership responsibilities with hands-on technical contribution, especially in data engineering, data infrastructure, or analytics engineering.
• Proven ability to lead multiple complex data initiatives from planning through execution and adoption.
• Deep experience leveraging experimentation, A/B testing, analytics, and metrics to inform decisions.
• Ability to align data strategy and execution with broader business, product, and engineering priorities.
• Ability to guide ML Engineering work in areas such as search, recommendations, personalization, or applied automation, even if not personally the deepest ML practitioner.
• Excellent communication and stakeholder management skills across technical, non-technical, and executive audiences.