R&D Analytics Engineer

Ontic

30d•$135,000 - $145,000•Remote

About The Position

Ontic is seeking a dedicated R&D Analytics Engineer to contribute to the development and scaling of the data and AI foundation for their enterprise SaaS business. This role is a key part of the centralized Operations organization, reporting to the Sr. Director of AI & Analytics. It is a hands-on position focused on architecting scalable analytics systems and AI-enabled workflows to support company-wide decision-making. The engineer will design core data models, architecture, and reusable assets for Product, Engineering & Design, aiming to redefine the analytics function in the age of AI. This is a foundational role with significant ownership and potential for long-term growth.

Requirements

3-6 years of experience in analytics engineering, data engineering, or similar hands-on roles.
Strong SQL skills and deep understanding of modern data modeling (dimensional modeling, transformations, data contracts, etc.).
Experience building in cloud data warehouses (Snowflake, BigQuery, Redshift, etc.).
Comfort working in a fast-moving, evolving AI tooling landscape.
Systems-level thinker who designs for scalability, extensibility, and flexibility.
Naturally curious and energized by solving ambiguous problems.
Pride in building clean, durable architecture — not just shipping quick fixes.
Strong communication skills and ability to collaborate across business and technical teams.

Nice To Haves

Experience with Fivetran, dbt, Snowflake, Hightouch, Omni.
Experience with AI & Knowledge Systems like Notion, Cursor, Glean.

Responsibilities

Design and maintain scalable dbt data models in Snowflake to serve as trusted sources of truth across the business.
Architect data pipelines and transformation workflows using Fivetran and modern ELT practices.
Develop durable, extensible data assets that evolve with company scaling.
Establish modeling standards, documentation practices, and governance frameworks.
Partner with RevOps and Finance to define and operationalize core SaaS metrics (ARR, retention, expansion, pipeline, etc.).
Build data foundations that power internal AI applications and LLM-enabled workflows.
Prototype and productionize AI-driven operational use cases.
Integrate analytics and AI into everyday workflows using tools such as Notion, Cursor, Glean, and other emerging platforms.
Design systems that embed data directly into operational processes, moving beyond just dashboards.
Partner with leaders across Sales, CS, Marketing, Finance, Product, and Engineering to translate business needs into scalable technical systems.
Create reusable data assets that enable activation via Hightouch and analysis via Omni.
Ensure architecture is scalable, secure, and adaptable to evolving enterprise requirements.