Data Validation & Processing Specialist

We are seeking a Data Quality & Processing Analyst responsible for managing, validating, and organizing field-collected datasets to ensure they meet strict quality, privacy, and submission standards. This role serves as a critical quality checkpoint between field data collection teams and client delivery, ensuring that datasets are accurate, structured, and ready for submission.

The ideal candidate will have experience handling large datasets, media files, structured data workflows, and detailed tracking systems, with a strong focus on accuracy and data integrity.

Required Qualifications

• Experience working with research, field, or media datasets within structured workflows.
• Strong proficiency in Google Sheets for tracking, validation, segmentation, and reporting.
• Proven ability to follow strict data labeling and validation protocols with high accuracy.
• Exceptional attention to detail with the ability to identify anomalies in large datasets.
• Strong written communication and documentation skills.

Technical Skills

• Ability to manage large media files (video, images, audio) including uploading, organizing, and validating files.
• Understanding of file systems, folder structures, and standardized naming conventions.
• Ability to validate metadata fields such as timestamps, IDs, and scenario labels.
• Experience identifying data integrity issues, including corrupted files, incomplete uploads, and duplicates.
• Familiarity with secure data handling practices, including access controls and privacy compliance.

Preferred Qualifications

• Experience working with camera-based, video, or scenario-driven data collection environments.
• Exposure to data QA processes and client acceptance criteria.
• Experience preparing datasets for internal or external review and delivery.

Performance Expectations

• High first-pass data acceptance rates with minimal rework.
• Zero critical submission failures due to labeling or metadata errors.
• Accurate, real-time tracking of dataset readiness and submission status.
• Early identification of data quality risks to avoid project delays.