Nudge Security AI Engineer & Data Systems Architect at Nudge Security owning AI rollout, enablement, and orchestration across business systems. Responsible for system integration, automation, and data architecture.
Responsibilities
Own how go-to-market and customer-facing platforms connect and integrate
Manage integrations, sync logic, data flow, and automation
Partner with platform administrators on configuration and process
Own data and system architecture to meet team needs
Advise on object and lifecycle modeling affecting data movement
Manage SaaS integration surface including scopes and access
Contribute integration, data, and AI-readiness assessments for tools
Qualification
5+ years in business systemsStrong SQLStrong written communicationdbt or similar transformation toolingExposure to SOC 2Your first 90 days30 days — Map the current state
Required
5+ years in business systems, revenue operations, analytics engineering, or a similar role — ideally at a B2B SaaS company.
Strong SQL. You’re comfortable in a cloud data warehouse, can model data and debug a pipeline, and know where the line is between “I’ll fix this” and “this needs an engineer.”
Practical experience with a CDP or event pipeline (Segment, RudderStack, or similar) and a product analytics tool (Mixpanel, Amplitude, or similar).
A track record of building reporting that people actually use, and of getting different systems to agree on the same number.
Genuine fluency with AI tooling — you’ve used it to automate real work, not just to draft emails.
Comfort operating in a security-conscious environment, and good instincts about data handling and access.
Strong written communication. Much of this job is translating between technical systems and the people who depend on them.
Preferred
Experience owning a financial planning and forecasting platform, and reconciling it against CRM and product data.
dbt or similar transformation tooling.
Exposure to SOC 2, ISO 27001, or comparable compliance programs.
Experience establishing data systems and processes at a growing company.
Your first 90 days
30 days — Map the current state: every system, every integration, every data flow, and every report anyone depends on. Identify what’s broken or untrusted.
60 days — Fix the highest-leverage data quality and reporting gaps. Establish a tracking plan and a review process with product and engineering.
90 days — Deliver a reporting layer the exec team trusts for forecasting and revenue, and ship your first meaningful AI-assisted automation.