Senior Analytics Engineer / Senior Data Ops Analyst

Location: Remote from Colombia, Argentina, Brazil, Chile, Costa Rica, Ecuador, Mexico, or Peru; LATAM-based and available during US business hours

Company

hirify.global supports startups and growth-stage companies with product engineering and global staffing, embedding vetted talent directly into client teams.

What you will do

• Monitor data quality throughout the pipeline and design scalable aggregate validation plans.
• Investigate discrepancies through hypothesis testing and root-cause analysis, focusing on preventive solutions.
• Own end-to-end data product releases for accuracy, reliability, and on-time delivery.
• Manage cross-functional squads and concurrent deadlines while communicating action plans and timelines to clients.
• Lead coverage expansion initiatives from scoping through delivery and partner with Commercial on customer-facing data solutions.
• Collaborate with Data Science and Engineering to close feasibility gaps and use AI to streamline workflows without adding QA overhead.

Requirements

• 6+ years of experience in data-focused roles.
• Expert SQL skills and experience mining large, frequently updated, multi-table datasets for inconsistencies.
• Experience designing data quality test plans that validate data reliably in aggregate.
• Advanced SQL, DBT, YAML, Regex, Excel, BigQuery, GCP, complex ETL, Looker, Redash, and Git.
• Experience collaborating directly with Engineering and Data Science, managing cross-functional projects, and making defensible judgment calls.
• Customer-facing experience translating client feedback into technically feasible solutions.

Nice to have

• Python, pandas, or PySpark exposure.
• Experience with panel, longitudinal, or subscription data.
• Background in data-as-a-product, market intelligence, or syndicated data.
• Experience managing data vendors and mentoring teammates on data operations practices.
• Experience in a small, remote-first team.

Culture & Benefits

• Full-time work in a remote-first analytics environment.
• Direct collaboration with client teams and business stakeholders.
• Exposure to messy data at scale, including billions of rows and frequent panel and configuration updates.
• Work alongside Data Operations, Commercial, Data Science, and Engineering functions.