Head of Data

Jobgether; United States; Remote; USD 200,000 - 260,000; Full time

Benefits offered by this job

Remote work option

Equity potential

Venture ecosystem exposure

Job summary

Partner Company (via Jobgether) seeks a Head of Data in the United States to lead data strategy for a rapidly scaling, ML-driven product in an early-stage venture.

You will oversee data engineering, data science, and analytics, build scalable data architectures, guide computer vision, recommendation engines, and augmented reality, and recruit and mentor a high-performing team while establishing best practices for data collection, modeling, testing and deployment.

Qualifications

• 8+ years of data engineering and/or data science experience.
• 5+ years leading data teams in high-growth environments.
• Experience with ML, computer vision, and AR is a plus.

Responsibilities

• Lead data organization with hands-on approach and set goals.
• Recruit, mentor, and manage data teams across disciplines.
• Align data initiatives with business objectives and partners.

Skills

Data leadership

Team building

Cloud platforms

ML systems

Communication

Education

Master's degree in Data Science, CS, or related field

Tools

AWS

GCP

Databricks

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Head of Data based in the United States.

The Head of Data will lead the data strategy and technical execution for a rapidly scaling, machine-learning-driven product in an innovative, early-stage venture environment. This role combines hands-on technical leadership with organizational development, overseeing data engineering, data science, and advanced analytics capabilities. You will build scalable data architectures, develop robust processing pipelines, and guide the application of computer vision, recommendation engines, and augmented reality. Working closely with executive leadership, engineering, product teams, and strategic partners, you will translate business objectives into effective data solutions. You will also recruit and develop a high-performing team while establishing best practices for data collection, modeling, testing, and deployment. This is an opportunity to shape a growing technology business from an early stage, supported by a broad ecosystem of expertise across product, engineering, design, and venture development.

Accountabilities

• Lead the data organization with a hands-on approach, establishing processes, playbooks, team goals, and strategic priorities.
• Recruit, build, manage, and mentor a high-performing, diverse team across data engineering, data science, and related disciplines.
• Align data initiatives with business objectives and coordinate expectations with internal teams, corporate partners, investors, and venture stakeholders.
• Establish data science, data engineering, and advanced analytics as core organizational capabilities.
• Design, build, and manage scalable data architectures, including data warehouses, data lakes, and data processing pipelines.
• Develop best practices for data identification, collection, capture, modeling, testing, and deployment.
• Build and oversee mobile and web-based data collection tools, data processing workflows, and predictive machine learning models.
• Guide the application of machine learning, particularly computer vision, recommendation engines, and augmented reality, across product and service offerings.
• Implement and maintain data privacy and security protocols to protect sensitive information and support regulatory compliance.
• Apply agile, lean, and continuous delivery practices to improve engineering efficiency, software quality, and operational scalability.
• Establish performance expectations, evaluate team effectiveness, and support employee development through coaching and feedback.
• Leverage the wider venture ecosystem for technical guidance, cross-functional collaboration, and connections that accelerate product development and business growth.

Requirements

• At least 8 years of experience in data engineering and/or data science.
• At least 5 years of experience leading, developing, and scaling data teams.
• Demonstrated success as a cross-functional people manager in an operationally complex, high-growth environment, balancing hands-on execution with effective resource allocation.
• Strong expertise in data storage, retrieval, and data architecture, including data warehouses, data lakes, and pipelines.
• Leadership experience in early-stage or venture-backed growth companies, ideally involving machine learning, computer vision, recommendation engines, or augmented reality.
• Experience in high-growth retail ventures, preferably in an early-stage leadership role.
• Experience with agile and lean methodologies and continuous delivery practices, including continuous integration, test-driven development (TDD), and infrastructure as code.
• Experience designing and implementing cloud-native software architectures.
• Proven ability to recruit, retain, and develop high-performing technical talent while fostering a values-driven, performance-oriented culture.
• Strong leadership presence, emotional intelligence, and a collaborative, low-ego approach.
• Exceptional communication and collaboration skills, with the ability to work effectively across technical teams, executive leadership, investors, and external partners.
• Ability to translate strategic business priorities into practical technical plans and scalable data capabilities.

Benefits

• Full-time, remote work opportunity, with preferred locations in San Francisco or Los Angeles, California.
• Opportunity to lead and scale the data function for a rapidly growing, machine-learning-driven product.
• Access to a broad venture ecosystem spanning product, engineering, design, analytics, marketing, legal, talent, finance, and venture capital.
• Opportunity to work closely with experienced executive leadership and strategic corporate partners.
• The potential to participate in meaningful equity-based rewards through the broader venture ecosystem.
• The opportunity to shape data strategy, technical architecture, and team culture from an early stage.
• Exposure to innovative technologies, including computer vision, recommendation engines, augmented reality, and predictive machine learning.
• A dynamic, cross-functional environment focused on building and scaling technology ventures.