Head of Data Infrastructure & Analytics

Puffy • Namer

Remote

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AI Summary

Lead data infrastructure and analytics for a high-growth D2C e-commerce business, focusing on building robust systems for data reliability and strategic decision-making. This role prioritizes preventing data disasters and architecting scalable solutions for a global market. Partner with executive leadership and cross-functional teams to establish industry-leading analytics operations.

Key Highlights

Architect and build data infrastructure with zero tolerance for errors, focusing on automated validation, data observability, and monitoring.

Lead a team to ensure data reliability drives confident decision-making, working closely with the CEO and cross-functional teams.

Own the technical stack that prevents data fires, including server-side tracking, attribution accuracy, and international expansion scaling.

Technical Skills Required

GA4 GTM Server-side tracking Data pipeline architecture E-commerce tracking SQL Python R ETL ELT Data warehousing API integrations Event-driven architecture Data observability tools (e.g., Monte Carlo, Great Expectations, dbt tests)

Benefits & Perks

Up to USD $200,000 per annum base salary

Up to 25% Performance Bonus

Total Annual Package: Up to USD $254,800

Remote work (US-based)

Comprehensive medical, dental, and vision insurance

Generous Paid Time Off (PTO) + Public holidays

AI-First organization with access to AI-native tool stack

Learning & development opportunities

International team collaboration

Job Description

Position: Head of Data Infrastructure & Analytics

Compensation: Up to USD $200,000 per annum + up to 25% Performance Bonus (Equivalent in local currency, based on your location)

Total Annual Package: Up to USD $254,800

Location: Remote

Role Overview

We're seeking a systems-obsessed data infrastructure leader to architect the analytics foundation that powers strategic decisions across our high-growth D2C business. This role is for builders who get more excited about preventing data disasters than presenting insights.

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