Tech Stack

Description

You will play a critical role in establishing a unified data foundation, enabling teams across product, finance, marketing, and revenue to make faster, data-driven decisions with confidence. Working in a fully remote, globally distributed environment, you'll collaborate with cross-functional stakeholders to transform complex business logic into clear, trustworthy data models. You will help shape the future of data practices by introducing standards, improving performance, and driving company-wide adoption of consistent metrics.

Requirements

• 7–10+ years of experience in analytics engineering, data engineering, or a related field, including time at a senior or staff level
• Advanced proficiency in SQL, with a strong focus on performance optimization, readability, and maintainability
• Deep experience with data warehousing technologies, particularly Redshift, including performance tuning and architectural design
• Strong expertise in dbt, including modular design, testing strategies, and scalable project organization
• Proven ability to build and maintain business-critical data models, including revenue recognition, churn, and lifecycle metrics
• Experience working in SaaS environments with complex subscription and product analytics requirements
• Excellent written communication skills, with the ability to document decisions, trade-offs, and architectural designs clearly
• Strong stakeholder management skills, with the ability to explain complex data concepts to non-technical audiences
• Analytical mindset with experience resolving ambiguous or contested metric definitions
• High level of autonomy, ownership, and systems thinking, with a proactive approach to problem-solving

Responsibilities

• Design and implement a scalable, reliable analytics architecture that serves as the single source of truth across all business functions
• Own and evolve the data modeling layer, ensuring consistency, performance, and maintainability across key domains such as finance, product, and marketing
• Lead the definition and standardization of canonical metrics, aligning stakeholders around shared, auditable business logic
• Audit and refactor existing data models to reduce inefficiencies, eliminate inconsistencies, and improve data quality
• Collaborate cross-functionally to translate business requirements into robust data solutions and reporting frameworks
• Drive experimentation and continuous improvement through performance optimization, testing, and documentation best practices
• Enable self-service analytics by building systems and documentation that empower teams to explore and trust data independently
• Mentor and influence data practices across the team, raising standards for analytics engineering and fostering a culture of rigor