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Mid LevelContractAI
Salary Range
$80,750 - $109,250 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
The Data Ops Specialist will support ongoing monitoring and quality assurance of large-scale data pipelines that rely on government website scraping. This role focuses on ensuring data collection systems remain accurate and reliable as external websites change.
Key Responsibilities
- →Monitor ongoing government website scraping pipelines to ensure data is collected accurately and consistently.
- →Perform regular QA checks on datasets and scraped data to identify broken links, structural changes, or inconsistencies.
- →Create and maintain evaluation benchmarks to assess scraping performance, dataset completeness, and pipeline reliability.
- →Analyze datasets and generate reports to track scraping progress and highlight anomalies or operational issues.
- →Manage project tracking across scraping initiatives, ensuring tasks, timelines, and deliverables are clearly organized and executed.
- →Communicate effectively with the engineering team when technical issues arise, ensuring problems are documented and resolved quickly.
Requirements
- ✓2+ years of professional experience in data operations, analytics support, QA, project coordination, or similar operational roles.
- ✓Familiarity with data pipelines, scraping workflows, or data QA processes is highly advantageous.
- ✓Strong attention to detail and the ability to identify inconsistencies in datasets and operational workflows.
- ✓Excellent organizational and project management skills for tracking tasks, deliverables, and timelines across multiple projects.
- ✓Strong written and verbal communication skills in English.
- ✓Ability to work independently and proactively follow issues through to resolution.
- ✓Comfort working with datasets, reporting tools, and structured documentation.
- ✓Basic analytical skills for evaluating datasets and identifying potential data issues.
- ✓Conscientious and methodical mindset with strong ownership over operational processes.
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