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Our vetted talent across LATAM and Asia embeds directly into client teams, operating as true extensions rather than external vendors.\nWith over 8 years working in venture-backed ecosystems, DevSavant is trusted to accelerate delivery, scale teams efficiently, and support companies as they reach their next milestone.\nAbout the Role\nWe are seeking a Senior Analytics Engineer / Senior Data Ops Analyst for the Data Operations team of a remote-first analytics company where the data itself is the product.\nThis is a senior individual contributor role owning the reliability, release, and customer-facing correctness of core data products. The numbers you sign off on are consumed by external clients, so the bar for defensible correctness is high. You will work with genuinely messy data at scale - billions of rows, weekly panel refreshes, daily configuration shifts - where the right answer is often a judgment call.\nLATAM-based, full-time, US business hours. Reports to the VP of Data Operations.\nKey Responsibilities\nData Quality\nMonitor data quality at each stage of the pipeline and build scalable test plans that validate data in aggregate\n\nDistinguish real signal from data-quality issues using hypothesis testing to rule out sources of discrepancy\n\nRun root-cause investigations that end in prevention, not just a fix\n\nRelease & Project Management\nOwn the end-to-end data product release: accurate, reliable, on-time\n\nProject-manage cross-functional squads across multiple concurrent deadlines\n\nCommunicate action plans and timelines directly to clients\n\nProduct & Collaboration\nTake DRI ownership of coverage expansion initiatives, from scoping to delivery\n\nPartner with Commercial to turn how customers use the data into frictionless solutions\n\nWork with Data Science and Engineering to close feasibility gaps\n\nUse AI to streamline workflows without adding QA overhead\n\nCore Technical Stack\nData: Advanced SQL, DBT, YAML, Regex, Excel\n\nWarehouse & Cloud: BigQuery, GCP (Cloud Storage, Dataproc), complex ETL\n\nBI: Looker, Redash, git\n\nPlus: Python, panel/longitudinal/subscription data, pandas or PySpark exposure\n\nRequired Qualifications\n6+ years in data-focused roles\n\nExpert SQL, with a track record of mining large datasets for inconsistencies\n\nExperience at real scale: complex multi-table environments with frequent update cycles, not static extracts\n\nExperience designing data quality test plans that hold up in aggregate\n\nComfort making defensible judgment calls when the outcome is genuinely unclear\n\nDirect collaboration with Engineering and Data Science\n\nCross-functional project management with accountability for business outcomes\n\nCustomer-facing experience translating client feedback into technical feasibility\n\nNice to Have\nManaging data vendors: selection, negotiation, issue resolution\n\nMentoring teammates on data operations best practices\n\nBackground in data-as-a-product, 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