Data QA Automation

Appdome's mission is to protect every mobile app in the world and the people who use them. We are the leader in AI-native mobile business protection, providing cyber and fraud teams with an agentic platform that builds, monitors, and maintains security defenses in Android and iOS apps — with no SDKs, no coding, and no disruption to engineering cycles.

Our platform delivers over 400 security, anti-fraud, anti-bot, and API protection capabilities, powered by deep learning models trained on a decade of mobile defense data and trillions of live threat events. From build time to runtime, Appdome's AI Agents help mobile brands detect, investigate, and respond to threats faster than ever — recognized as the best AI Platform for Cyber Resilience at RSA Conference 2026 for the second consecutive year.

Leading financial, healthcare, m-commerce, and B2B brands rely on Appdome to secure over 50,000 mobile apps and protect more than 1 billion end users globally.

Appdome is an Equal Opportunity Employer. We are committed to diversity, equity, and inclusion in our workplace. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law. All qualified applicants will receive consideration for employment without regard to any of these characteristics.

About the Role

We are looking for a Data QA Automation with strong experience in data validation, automation, analytics testing, and large-scale event pipeline quality.

This role is focused on ensuring the accuracy, stability, and reliability of data across our analytics and security event systems. The ideal candidate will be responsible for building automated validations, investigating data discrepancies, monitoring data quality, and working closely with engineering, data, and product teams.

Responsibilities

• Design, build, and maintain automated validation processes for large-scale event pipelines.
• Validate end-to-end data flows across ingestion, processing, storage, and dashboard layers.
• Create SQL-based validations to verify event counts, unique devices, metadata accuracy, schema
• consistency, latency, and data freshness.
• Investigate discrepancies between production systems, staging environments, data warehouses, object
• storage, and customer-facing dashboards.
• Monitor event volume, data latency, anomalies, spikes, drops, duplicates, and missing data.
• Build and maintain CI/CD validation jobs using tools such as Jenkins or GitLab CI.
• Create clear automated reports, dashboards, and email summaries for validation results.
• Work closely with backend engineers, data engineers, QA teams, and product stakeholders to identify,
• report, and validate fixes for data quality issues.
• Support performance and scalability testing for analytics dashboards, queries, and data pipelines.
• Help improve internal data assurance processes, data observability, and production monitoring.

Requirements

• 2+ years of experience in Data Assurance, Data Validation, QA Engineering, or a similar role.
• Strong hands-on experience with SQL and data validation.
• Experience testing or validating analytics systems, event pipelines, ETL/ELT processes, or high-volume data platforms.
• Experience with automation using JavaScript/Node.js, Python, or another programming language.
• Ability to investigate complex data issues across multiple systems.
• Good understanding of APIs, logs, databases, object storage, and data processing flows.
• Experience creating automated reports or validation summaries.
• Strong analytical thinking, attention to detail, and ownership mindset.

Advantages

• Experience with ClickHouse, Athena, S3, Kafka, Metabase, or similar technologies.
• Experience with Playwright or other automation frameworks.
• Experience validating Parquet files, schema consistency, and large-scale event data.
• Experience with security analytics, event pipelines, device identifiers, or customer-facing analytics dashboards.
• Experience with CI/CD tools such as Jenkins, GitLab CI, or similar.
• Experience with Docker and AWS services.