Software Quality Assurance Engineer

Job Title: Analytics QA Engineer

Location: LATAM - Remote

Mode: Contract

Job Description:

Data plays a central role as part of the Data & Insights Group (DIG), our Measurement Operations (MOPS) team plays a crucial part in the success of our streaming products! It drives business, product, and operational decisions by providing rich data, strategic insights, and analytical products. We are looking for an Analytics QA Engineer for client's straming who is passionate about data and entertainment, and who thrives in a dynamic and fast-paced environment.

MOPS Tracking & Big Data Quality Engineer

Level: Mid / Senior

About the Role

• We are looking for a MOPS Tracking & Big Data Quality Engineer to ensure that measurement data is accurately captured, processed, modeled, and made available for trusted analytics across our streaming products.
• This hybrid role sits at the intersection of measurement quality, big data engineering, analytics engineering, and business intelligence. You will validate application and platform tracking from the point of event generation through ingestion, transformation, storage, semantic modeling, and downstream reporting.
• You will partner closely with client engineering, tracking engineering, data engineering, analytics, product, and business intelligence teams to ensure that events, metadata, metrics, and datasets remain accurate and consistent throughout the data lifecycle.
• The ideal candidate has strong SQL and data-validation skills, understands digital event tracking and large-scale data pipelines, and can build or support governed semantic models that provide consistent, trusted business metrics.

What You’ll Do

1. Measurement and End-to-End Data Quality

Critical responsibilities:

• Validate event tracking across web, mobile, connected TV, and streaming platforms against approved specifications and business requirements.
• Perform end-to-end validation from application-generated events through ingestion, transformation, storage, semantic layers, and reporting.
• Validate ETL and ELT pipelines using SQL-based checks for data accuracy, completeness, consistency, freshness, and duplication.
• Reconcile data across source events, BigQuery datasets, curated marts, governed database tables, semantic models, and BI reporting.
• Identify tracking gaps, payload discrepancies, schema drift, transformation errors, data loss, and incorrect metric attribution.
• Validate that KPIs and business metrics remain consistent and traceable across dashboards, reports, APIs, and analytics use cases.
• Partner with engineering and analytics teams to troubleshoot issues, determine root causes, and communicate release and data-quality risks.

2. Data Quality Engineering, Automation, and Governance

Critical responsibilities:

• Develop reusable SQL queries, automated tests, and reconciliation frameworks for event data, pipelines, data stores, and semantic models.
• Build monitoring, dashboards, and alerts to proactively detect anomalies and data-quality degradation.
• Validate BigQuery dataset structure, partitioning, lineage, accessibility, performance, and data freshness.
• Support CI/CD and data-observability practices that increase validation coverage and reduce manual testing.
• Translate product, measurement, analytics, and business requirements into clear technical validation criteria.
• Partner with data engineers and business stakeholders on schemas, transformations, KPI definitions, and governed data access.
• Maintain essential documentation for tracking requirements, test strategies, data lineage, validation results, and operational procedures.
• Clearly communicate defects, risks, quality findings, and remediation status to technical and nontechnical stakeholders.

Key Projects

• End-to-end validation of streaming measurement data from application tracking through ingestion, transformation, warehousing, semantic modeling, and reporting.
• Automated testing for critical event streams, pipelines, curated marts, and business metrics.
• Data-quality monitoring and anomaly detection for high-volume measurement datasets.
• Validation of datasets supporting executive dashboards, audience reporting, content performance, advertising, and product analytics.
• Development of governed semantic models and reusable metrics for self-service analytics.
• Reconciliation of metrics across source events, BigQuery datasets, semantic models, and downstream reporting.
• Validation of new platform launches, tracking migrations, schema changes, and pipeline modernization initiatives.

Minimum Qualifications

• 3+ years of experience in data quality engineering, big data QA, analytics engineering, BI development, or a related field.
• Strong SQL skills and experience validating large datasets in a cloud data warehouse such as BigQuery, Snowflake, or a comparable platform.
• Experience testing ETL or ELT pipelines, transformations, and data-warehouse outputs.
• Experience with digital analytics, event tracking, telemetry, or application measurement data.
• Experience building or supporting governed semantic models and reusable business metrics.
• Understanding of dimensional modeling, including facts, dimensions, star schemas, and curated data marts.
• Experience developing test plans, validation queries, reconciliation processes, and automated data checks.
• Familiarity with Git-based development and validation workflows.
• Ability to translate business and analytics requirements into maintainable technical solutions and test specifications.
• Strong troubleshooting, analytical, communication, and documentation skills.

Preferred Qualifications

• Experience validating measurement data across web, mobile, connected TV, or streaming applications.
• Familiarity with analytics and data-collection technologies such as Adobe Analytics, Adobe Experience Platform, Tealium, or comparable tools.
• Experience with GCP, BigQuery, AWS, and cloud-based data-processing environments.
• Experience building automated data-quality frameworks or integrating tests into CI/CD pipelines.
• Knowledge of semantic-layer architecture, metric governance, and self-service analytics.
• Familiarity with APIs, embedded analytics, or AI and natural-language data interfaces.
• Knowledge of data observability, anomaly detection, lineage, cataloging, and dataset certification.
• Familiarity with performance tuning, aggregation strategies, caching, and query optimization.
• Experience with data governance, privacy, access controls, PII handling, and row-level security.
• Experience with Python or another scripting language for data validation and automation.

What Success Looks Like

• Within the first three months, you will be independently validating measurement data across critical pipelines, identifying and resolving data-quality issues, and contributing production-ready SQL, automated checks, and semantic-layer improvements.
• You will establish strong partnerships across MOPS, client engineering, data engineering, analytics, and BI teams while improving the consistency and transparency of end-to-end data validation.
• Over time, your work will help create a trusted measurement ecosystem in which events are implemented correctly, pipelines remain observable, metrics reconcile across systems, and stakeholders can confidently use dashboards and self-service analytics to make business decisions.
• Knowledge of release and regression testing methodologies
• Ability to define standard methodologies for testing tools