Senior Data Operations Analyst

About DevSavantDevSavant is an operating partner for startups and growth-stage companies, helping them turn ambition into execution.We support founders and leadership teams with product engineering and global staffing, from early prototypes and MVPs to scaling high-performing teams. Our vetted talent across LATAM and Asia embeds directly into client teams, operating as true extensions rather than external vendors.With 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. About the RoleWe 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.This 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.LATAM-based, full-time, US business hours. Reports to the VP of Data Operations. Key ResponsibilitiesData QualityMonitor data quality at each stage of the pipeline and build scalable test plans that validate data in aggregateDistinguish real signal from data-quality issues using hypothesis testing to rule out sources of discrepancyRun root-cause investigations that end in prevention, not just a fixRelease & Project ManagementOwn the end-to-end data product release: accurate, reliable, on-timeProject-manage cross-functional squads across multiple concurrent deadlinesCommunicate action plans and timelines directly to clientsProduct & CollaborationTake DRI ownership of coverage expansion initiatives, from scoping to deliveryPartner with Commercial to turn how customers use the data into frictionless solutionsWork with Data Science and Engineering to close feasibility gapsUse AI to streamline workflows without adding QA overheadCore Technical StackData: Advanced SQL, DBT, YAML, Regex, ExcelWarehouse & Cloud: BigQuery, GCP (Cloud Storage, Dataproc), complex ETLBI: Looker, Redash, gitPlus: Python, panel/longitudinal/subscription data, pandas or PySpark exposureRequired Qualifications6+ years in data-focused rolesExpert SQL, with a track record of mining large datasets for inconsistenciesExperience at real scale: complex multi-table environments with frequent update cycles, not static extractsExperience designing data quality test plans that hold up in aggregateComfort making defensible judgment calls when the outcome is genuinely unclearDirect collaboration with Engineering and Data ScienceCross-functional project management with accountability for business outcomesCustomer-facing experience translating client feedback into technical feasibilityNice to HaveManaging data vendors: selection, negotiation, issue resolutionMentoring teammates on data operations best practicesBackground in data-as-a-product, market intelligence, or syndicated dataPrior experience in a small, remote-first team