Analytics Engineer

Ontic

8d•$95,000 - $105,000•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 hands-on position focused on architecting scalable analytics systems and AI-enabled workflows that support company-wide decision-making. The Analytics Engineer will design core data models, architecture, and reusable assets for Product, Engineering, and Design teams, while also helping to redefine the analytics function in the context of AI. This is a foundational role with significant ownership and opportunities for long-term growth.

Requirements

Experience in analytics engineering, data engineering, or similar hands-on roles.
Strong SQL skills and a 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 thinking with a focus on scalability, extensibility, and flexibility.
Natural curiosity and energy for solving ambiguous problems.
Pride in building clean, durable architecture.
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.
Strong fundamentals and the ability to quickly learn new technologies.

Responsibilities

Design and maintain scalable dbt data models in Snowflake to serve as trusted sources of truth.
Architect data pipelines and transformation workflows using Fivetran and modern ELT practices.
Develop durable and extensible data assets that can scale with the company.
Establish modeling standards, documentation practices, and governance frameworks.
Partner with RevOps and Finance to define and operationalize core SaaS metrics (e.g., ARR, retention, expansion, pipeline).
Build data foundations to 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 like Notion, Cursor, and Glean.
Design systems that embed data directly into operational processes, moving beyond traditional dashboards.
Partner with leaders across Sales, CS, Marketing, Finance, Product, and Engineering to translate business needs into scalable technical systems.
Create reusable data assets for activation via Hightouch and analysis via Omni.
Ensure architecture is scalable, secure, and adaptable to evolving enterprise requirements.