Job Overview
Location
New York
Job Type
Full-time
Category
Data Science
Date Posted
May 26, 2026
Full Job Description
đź“‹ Description
- • Design and build scalable data models and pipelines using dbt to transform raw data into clean, reliable assets that power company-wide financial analytics and decision-making.
- • Define and implement a robust semantic layer (e.g., LookML/Omni/Other) that standardizes financial and operating metrics including revenue, retention, customer growth, usage, margin, and forecast inputs.
- • Partner cross-functionally with Product, Finance, and the Executive Team to deliver intuitive, consistent dashboards and analytical tools that surface business health metrics such as ARR and NRR.
- • Establish and champion data modeling standards and best practices, guiding the organization in modeling data for accuracy, performance, usability, and long-term maintainability.
- • Lead data governance initiatives to ensure high standards of data quality, consistency, documentation, and access control across the analytics ecosystem.
- • Structure financial metric definitions, business logic, and accounting context to support AI-assisted reporting, natural language analytics, and automated anomaly detection.
- • Work with SQL, dbt, Python, and Snowflake to develop and maintain financial data pipelines and analytical assets.
- • Utilize modern BI tools such as Looker or Omni to create and maintain financial dashboards and reporting interfaces.
- • Apply version control (GitHub) and modern development workflows including CI/CD to manage data code and deployments.
- • Define and resolve inconsistencies in financial and operational data across complex systems.
- • Model financial, billing, subscription, CRM, and usage-based revenue data with precision and scalability.
- • Maintain a strong understanding of business metrics including ARR, MRR, churn, retention, expansion, bookings, billings, and revenue recognition.
- • Collaborate with Finance, GTM, Product, and Executive stakeholders to build trusted partnerships and translate business needs into data solutions.
- • Operate in a fast-moving, ambiguous environment with a bias for action, prioritizing iterative delivery of usable data models over waiting for perfect solutions.
- • Balance big-picture strategic thinking with meticulous attention to execution details, knowing when to move quickly and when to deepen analysis.
🎯 Requirements
- • 5+ years of experience in Analytics Engineering, Data Engineering, Data Science, or similar field.
- • Deep expertise in SQL, dbt, Python, and Snowflake.
- • Experience with modern BI tools such as Looker, Omni, or similar.
- • Strong familiarity with version control (GitHub), CI/CD, and modern development workflows.
- • Experience modeling financial, billing, subscription, CRM, or usage-based revenue data.
- • Strong understanding of business metrics such as ARR, MRR, churn, retention, expansion, bookings, billings, and revenue recognition.
🏖️ Benefits
- • Compensation range of $155,000 - $235,000 USD depending on location.
- • Opportunity to work at a hyper-growth startup with 1500+ customers in 60+ countries.
- • Access to world-class investor support and a company defined by decisiveness, simplicity, and a mission-driven culture.
- • Exposure to frontier agentic AI and enterprise-grade platform technologies shaping the future of professional services.
- • Commitment to equal opportunity and reasonable accommodations for applicants with disabilities.
- • Participation in a culture that values ownership, intensity, and continuous improvement.
Skills & Technologies
About Harvey AI Inc.
Harvey AI Inc. provides a generative artificial-intelligence platform engineered specifically for the legal profession. The software integrates with law-firm workflows to automate contract drafting, review, due-diligence and regulatory research, producing lawyer-quality language grounded in up-to-date statutes and precedents. Harvey combines large language models trained on legal corpora with secure, private-cloud deployment and firm-specific fine-tuning to maintain confidentiality and compliance. Clients range from global law firms to in-house legal departments seeking efficiency gains without compromising accuracy or security. The company was founded in 2022 and is headquartered in San Francisco, California.
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