Middle Data Test Engineer

Founded in 2013, our client is a hugely popular, cutting-edge online streaming television company from California. This free broadcasting service provides millions of viewers with more than 100 exclusive live and original channels that users won’t find on local TV. The content is full of TV shows of all kinds, premium news, on-demand movies, cartoons, viral videos, sports, lifestyle and trending digital series. In a nutshell, there is literally everything you’d like to see on your screen – hand-picked by people who know and love the entertainment industry.

More so, they offer a free app that runs seamlessly on multiple mobile, web and various connected TV streaming devices. Well-known online giants The Verge, Venture Beat, and Vibe declared our customer as ‘awesome’. So, we can say for sure that our client is redefining the future of television.

Our client is led by a seasoned team of entrepreneurs, TV and technology executives, and top-of-class team members. $43M+ funding with investors like US Venture Partners, Sky, Chicago Ventures, Universal Music Group, United Talent Agency and Pritzker Group. Top app with millions of users across platforms like web, iOS, Android, Roku, Apple TV, Amazon Fire TV, Android TV, Vizio, Samsung, and more.

One of the hottest startups in Los Angeles and one of the leading video platforms in the industry -often covered in NY Times, Forbes, Techcrunch, Variety, USA Today, CNN, Mashable and more!

High energy culture. Passionate entrepreneurial team. Massive market. Employing the best of the best in the industry. We’re building the future of television.

#We are on a mission to unleash the power of content… you in?

We’ve got the brands, we’ve got the stars, we’ve got the power to achieve our mission to entertain the planet – now all we’re missing is… YOU! Joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture.

• Interview process that respects people and their time
• Professional and open IT community
• Internal meet-ups and resources for knowledge sharing
• Time for recovery and relaxation
• Bright online and offline events
• Opportunity to become part of our internal volunteer community

• 5+ years of experience in QA, with a specific focus on big data quality assurance and analytics.
• Proficiency with big data tools, particularly Google BigQuery, and experience with SQL for data validation.
• Experience with ETL process validation, ensuring data accuracy and consistency across stages.
• Familiarity with cloud environments (GCP, AWS), and understanding of how data is ingested, transformed, and queried within these ecosystems.
• Strong analytical skills to detect data discrepancies, troubleshoot issues, and maintain data integrity across high-volume datasets.
• Excellent communication skills to convey data quality insights to both technical and non-technical stakeholders.
• Hands-on experience with data quality tools, monitoring frameworks, and scripting languages (e.g., Python, Shell scripting) to automate QA tasks.
• Experience with data pipeline tools (e.g., Apache Beam, Dataflow, Spark).
• Knowledge of Agile methodologies, JIRA, and Confluence.
• Previous experience with BI tools (Tableau, Looker) for validating data used in business intelligence reporting.
• Background in media or entertainment industries and familiarity with streaming analytics.

GenAI Responsibilities & Must‑Have Skills (Combined)

• Claude is mandatory – hands‑on to create/optimize data validation rules, queries, reconciliation logic, and analysis summaries.
• Automate data validation to reduce manual effortd 
• Implement AI to shift data validation from manual checks to automated and intelligent analysis frameworks.
• Use GenAI for anomaly detection, trend analysis, and data quality insights to speed up triage and root cause identification.
• Build RAG‑enabled support using data dictionaries, pipeline specs, historical defects to keep AI outputs accurate.
• Familiarity with Agentic AI frameworks for automated workflows (metadata fetch → validation → summary → next action).
• Hands‑on Docker to package and run validation utilities reliably across environments.
• Strong Big Data testing fundamentals: ETL/ELT validation, schema checks, reconciliation, and data quality dimensions.