QA Analyst (Digital Advertisement BPO)

Job Description

Roles & Responsibilities

Quality Review & Interaction Auditing

•Conduct systematic QA evaluations of support interactions — including tickets, live chats, email threads, and escalation cases — assessing accuracy, technical correctness, process adherence, and communication quality against defined QA rubrics.

•Audit technical integration support cases involving API failures, tracking discrepancies, SDK issues, and payload errors to ensure agents are following correct diagnostic and resolution procedures.

•Perform targeted calibration reviews of high-complexity cases involving billing disputes, conversion attribution discrepancies, and ad policy escalations to validate consistency across the support team.

•Identify patterns and trends in QA defects — including recurring knowledge gaps, process deviations, and documentation errors — and escalate findings to the QA Lead and Training team.

•Maintain a structured audit log of all reviewed interactions, ensuring accurate scoring, fair calibration, and a defensible record of QA outcomes.

2. SQL-Based Data Quality & Analytics QA

•Write and execute advanced SQL queries to validate data pipeline outputs, verify conversion event integrity, cross-check attribution data, and audit reporting accuracy across platform databases.

•Identify data anomalies, duplicate event records, missing conversion signals, and attribution calculation errors through systematic SQL-driven investigation and root-cause analysis.

•Build and maintain SQL-based QA dashboards and reporting scripts that surface team-level quality metrics, defect frequency trends, and SLA compliance data for operational reporting.

•Validate the accuracy of SQL queries written by support agents and analysts, reviewing query logic for correctness, efficiency, and potential data integrity risks.

•Support QA-driven data investigations by querying event-level data from ad platforms to reconcile discrepancies between reported conversion figures and raw tracking data.

3. API & Integration Quality Validation

•Review and validate API integration implementations — including RESTful APIs, webhooks, and server-side tracking configurations — to ensure they conform to platform documentation, schema requirements, and expected behavioral standards.

•Audit API request and response payloads in JSON and XML formats to identify malformed data, missing required parameters, schema mismatches, incorrect authentication headers, and error code handling failures.

•Evaluate the quality of technical escalation handling for API-related issues, verifying that support agents correctly interpret API documentation, apply the right debugging methodology, and communicate resolutions accurately.

•Test and validate webhook event delivery, retry logic, and payload structure during integration QA cycles to ensure real-time data flows are operating as expected.

•Review SDK implementation QA reports and validate event instrumentation correctness across web and mobile platforms by cross-referencing raw event logs with expected implementation specs.

4. Conversion Tracking & Attribution QA

•Perform end-to-end quality validation of conversion tracking setups — including pixel-based, server-side, and hybrid event architectures — to confirm data completeness, deduplication accuracy, and event schema compliance.

•Review and validate attribution model outputs, cross-checking multi-touch attribution calculations, attribution window configurations, and conversion path logic against expected platform behavior and client specifications.

•Audit conversion event data for signal quality issues including delayed event firing, mismatched event names, incorrect value parameters, and missing required fields that could distort attribution or ROAS reporting.

•Conduct QA reviews of marketing analytics reports to verify that funnel metrics, ROAS figures, CPA calculations, and audience insights are derived from accurate, well-validated data sources.

•Flag and document conversion tracking or attribution discrepancies, working with Engineering and Data teams to trace root causes and ensure corrective actions are validated before closing the QA finding.

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