Success Analytics Engineer

Responsibilities

• Build scheduled pipelines from billing, product telemetry, support, and CRM systems into the company data warehouse.
• Develop and operate nightly account-scoring jobs that classify accounts and detect meaningful changes.
• Build a real-time service that triages account-risk events and routes them to automated or human follow-up.
• Create campaign audience synchronizations with experiment controls, along with digests, reporting views, and internal-console data layers.
• Design, deploy, and operate AI workloads for language classification, extraction, and drafting with cost and reliability controls.
• Ship the initial pipeline and scoring job, establish the real-time triage service with alerting and runbooks, and support the first automated campaign cycle.

Requirements

• 4+ years of experience in data, analytics, or backend engineering, including production data warehouse experience with SQL, dbt or similar, and Python.
• Experience building and operating scheduled pipelines and webhook-driven services in production.
• Experience taking products from development through production deployment and operating them for real users.
• Experience building or heavily operating customer-success tooling, CRM, marketing automation, or support systems.
• Deep experience designing AI workflows, deploying and operating different models in production, and selecting appropriate models for each task.
• Ability to ship weekly, sequence work against a written roadmap, and validate and reconcile data proactively.