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Infrastructure Analyst (Data Creation Owner) - A251 at Pearl. Industry. Technical / Data Infrastructure. Work Arrangement. Remote. Job Type. Full-time. Work Schedule. Standard business hours with required overlap with US Pacific Time (PST). Locations:. . LATAM:. Mexico City (Mexico), Bogotá (Colombia), São Paulo (Brazil), Buenos Aires (Argentina), Caracas (Venezuela), Honduras (Dominican Republic). Anywhere in . LATAM. About Pearl Talent. Pearl works with the top 1% of candidates from around the world and connects them with the best startups in the US and EU. Our clients have raised over $5B in aggregate and are backed by companies like OpenAI, a16z, and Founders Fund. They’re looking for the sharpest, hungriest candidates who they can consistently promote and work with over many years. Candidates we’ve hired have been flown out to the US and EU to work with their clients, and even promoted to roles that match folks onshore in the US.. Hear why we exist, what we believe in, and who we’re building for: . WATCH HERE. Why Work with Us?. At Pearl, we’re not just another recruiting firm—we connect you with exceptional opportunities to work alongside visionary US and EU founders. Our focus is on placing you in roles where you can grow, be challenged, and build long-term, meaningful careers.. About the Company. Our client is a technology-driven company operating at the intersection of data, infrastructure, and machine learning. They build large-scale datasets that power advanced analytics and AI systems, helping organizations make better decisions using real-world visual and geospatial information. The company operates in a fast-paced, high-ownership environment where data quality and execution excellence are critical to customer success.. Role Overview. The Infrastructure Analyst (Data Creation Owner) is responsible for end-to-end ownership of a complex, judgment-driven data creation pipeline built from roadway video and imagery. This role ensures that infrastructure features such as roadway assets, signage, and surface conditions are accurately identified, classified, and delivered to customers at scale.. You will define standards, resolve ambiguous edge cases, and act as the final quality authority for datasets. This is a highly accountable role that works cross-functionally with Machine Learning and Customer Success teams while leading operational annotation teams. The ideal candidate is decisive, detail-oriented, and thrives in environments where speed, accuracy, and ownership must be carefully balanced.. Your Impact. You will ensure the delivery of high-quality, customer-ready datasets that directly support machine learning performance and customer outcomes. Your work will reduce ambiguity, improve data consistency, and increase delivery reliability across large-scale infrastructure datasets. You will help scale data operations while maintaining strict quality standards, enabling faster iteration and stronger customer satisfaction. Your ownership will directly influence operational efficiency, data accuracy, and long-term scalability.. Core Responsibilities. End-to-End Data Ownership – 35%. Own the full data creation lifecycle from video pre-processing through tagging, QA, and final delivery. . Ensure datasets meet strict accuracy, consistency, and customer-defined requirements. . Act as the final decision-maker on ambiguous classifications and edge cases. . Quality & Standards – 25%. Define, document, and continuously refine data creation and annotation standards. . Build and maintain QA frameworks that ensure repeatable, high-quality output. . Identify recurring quality issues and implement corrective processes. . Team Leadership – 20%. Lead and manage annotation and review teams to meet quality and delivery targets. . Set performance benchmarks and provide clear feedback to maintain consistency. . Scale output while preserving quality standards. . Cross-Functional Collaboration – 10%. Partner closely with Machine Learning teams to align datasets with real-world requirements. . Work with Customer Success teams to ensure timely and accurate dataset delivery. . Translate customer needs into clear internal execution standards. . Process Improvement & Scale – 10%. Identify opportunities to automate or streamline tagging, QA, and review workflows. . Build systems and processes that support growing dataset volume and complexity.. Must-Haves (Required). 4+ years of experience owning or operating complex, judgment-driven data or content pipelines. . Proven accountability for both data quality and delivery timelines. . Strong project management, prioritization, and execution skills. . Experience collaborating closely with Machine Learning engineers and technical stakeholders. . Demonstrated experience leading or managing operational teams (annotators, reviewers, or QA). . Excellent written and verbal English communication skills.. Nice-to-Haves (Preferred). Google Workspace . Spreadsheet tools (Google Sheets or Excel) . Project management tools. Tools Proficiency. Must-Haves (Required). Google Workspace . Spreadsheet tools (Google Sheets or Excel) . Project management tools. Nice-to-Haves (Preferred). Annotation or labeling platforms . Data QA or review tools . Workflow automation tools. Company Location: Venezuela, Bolivarian Republic of.