Analytics Engineer

Ontic

About The Position

Ontic provides software that helps corporate and government security teams identify threats, assess risk, and respond faster to keep people and organizations safe. Its Connected Intelligence Platform unifies security operations and data into a centralized system of record, enabling organizations to conduct risk assessments, protect against workplace violence, and manage threats and incidents more efficiently. Fortune 500 companies and federal agencies rely on Ontic to support security programs such as executive protection, threat intelligence, and corporate investigations. Learn more at ontic.co or follow us on LinkedIn. We are hiring our first dedicated Analytics Engineer to help build and scale the data and AI foundation of our enterprise SaaS business. Reporting to the Sr. Director of AI & Analytics within our centralized Operations organization, this is a highly hands-on role focused on architecting scalable analytics systems and AI-enabled workflows that power decision-making across the company. You will design the core data models, architecture, and reusable assets that serve Sales, Customer Success, Marketing, Finance, Product, and Engineering — while helping redefine what an analytics function looks like in the age of AI. This is a foundational hire with high ownership and long-term growth potential.

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

Responsibilities

Enterprise Data Modeling & Architecture
Design and maintain scalable dbt data models in Snowflake that 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 as the company scales
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.)
AI-Enabled Systems & Workflow Integration
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 — not just dashboards
Cross-Functional Analytics Infrastructure
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