Qualifications:
Education
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a closely related technical discipline.
- Advanced degree (MBA or M.S. in a technical or analytics field) is a plus and may substitute for a portion of the experience requirement.
Experience
- Minimum 2 years of experience in backend product support, ad tech engineering support, technical integrations, or QA in a technical setting.
- Strong preference for SaaS experience, with knowledge of platform lifecycle, API-first products, and multi-tenant architectures.
- Proven growth into leadership, QA management, or senior technical roles.
- Experience in developing or enhancing QA programs in support, BPO, or digital advertising is a plus.
Technical Skills
- SQL & Data Analysis: Expert in complex SQL queries, query optimization, and data analysis for pipeline validation, event auditing, conversion tracking, and executive reporting.
- Proficient with BI tools (Looker, Tableau, Power BI, Mode) for QA dashboards and leadership reporting.
- APIs, Server-Side Tracking & SDKs: Skilled in RESTful APIs (HTTP methods, OAuth 2.0, API keys, JWT, rate limiting, pagination, error handling, versioning).
- Experienced in server-side tracking, webhook setup, event pipelines, SDK validation, and QA review of instrumentation on web and mobile platforms.
- Expertise in API payload debugging (JSON/XML).
Roles & Responsibilities:
1. QA Program Leadership & Strategy: Design and continuously improve the end-to-end QA program for ad tech support and integration operations, including frameworks, scoring, evaluation, and calibration.
2. Team Management & Analyst Development: Manage and mentor a team of Quality Analysts by setting clear expectations, conducting reviews, and creating growth plans aligned with technical career paths.
3. Quality Evaluation Framework Design: Develop and maintain QA rubrics and scoring matrices to assess support interactions for technical accuracy, resolution completeness, process adherence, communication, and customer experience.
4. SQL-Driven Quality Analytics & Reporting: Use advanced SQL to extract and analyze quality data from support systems, event databases, and tracking platforms, identifying defect patterns, resolution trends, and agent performance.
5. API & Integration Quality Oversight: Manage QA coverage for API and integration support cases, ensuring rubrics, test cases, and training address RESTful API, webhook, and SDK workflows.