Data Testing Demand

Type: Contract, per-project.
Location: Remote - within LATAM time zones (GMT-3 to GMT-5) - Remote with meetings across U.S. time zones
Availability: Contractor (40 hours per week)

Required Skills

• 5+ years of experience in QA, with a strong focus on big data quality assurance and analytics.

• Proficiency with big data tools, particularly Google BigQuery, and strong experience using SQL for data validation.

• Hands-on experience validating ETL/ELT processes, ensuring data accuracy and consistency across all stages.

• Familiarity with cloud environments such as Google Cloud Platform and Amazon Web Services, with a strong understanding of how data is ingested, transformed, and queried within these ecosystems.

• Strong analytical skills to identify data discrepancies, troubleshoot issues, and maintain data integrity across high-volume datasets.

• Excellent communication skills, with the ability to convey data quality insights to both technical and non-technical stakeholders.

• Hands-on experience with data quality tools, monitoring frameworks, and scripting languages such as Python and Shell scripting to automate QA tasks.

• Experience with data pipeline tools such as Apache Beam, Google Cloud Dataflow, and Apache Spark.

• Solid understanding of Agile methodologies and experience using Jira and Confluence.

• Experience with BI tools such as Tableau and Looker for validating data used in business intelligence reporting.

• Previous experience in media or entertainment industries, with familiarity in streaming analytics.

GenAI Responsibilities & Must-Have Skills

• Hands-on experience with Claude is mandatory, including creating and optimizing data validation rules, queries, reconciliation logic, and analysis summaries.

• Experience automating data validation processes to reduce manual effort and improve efficiency.

• Ability to implement AI-driven solutions to shift data validation from manual checks to intelligent, automated analysis frameworks.

• Experience using GenAI for anomaly detection, trend analysis, and generating data quality insights to accelerate triage and root cause analysis.

• Experience building RAG-enabled support systems using data dictionaries, pipeline specifications, and historical defects to improve AI output accuracy.

• Familiarity with Agentic AI frameworks for automating workflows (e.g., metadata retrieval → validation → summary → next action).

• Hands-on experience with Docker to package and run validation utilities consistently across environments.

• Strong foundation in big data testing, including ETL/ELT validation, schema validation, reconciliation, and data quality dimension testing.