Senior Data Operations Analyst

Role overview

A remote-first analytics company is hiring a senior individual contributor to own the reliability, release, and customer-facing correctness of its core data products. The position sits at the intersection of analytics engineering, data operations, and product delivery, working with panel-scale datasets in the billions of rows. It is a full-time role based in Latin America with US business hours, reporting into the VP of Data Operations.

Responsibilities

- Monitor data quality across every stage of the pipeline and design scalable, aggregate-level test plans that catch issues before they reach clients. - Use hypothesis testing and root-cause investigation to distinguish real signal from data-quality artifacts, aiming for prevention rather than one-off fixes. - Drive the end-to-end release of data products, holding the line on accuracy, reliability, and on-time delivery. - Coordinate cross-functional squads across multiple concurrent deadlines and communicate action plans and timelines directly to external customers. - Take ownership of coverage expansion initiatives from scoping through delivery, partnering with Commercial teams to translate customer usage into product improvements. - Work with Data Science and Engineering to close feasibility gaps and apply AI tools to streamline workflows without adding QA overhead.

Requirements

- Six or more years in data-focused roles with expert-level SQL and a demonstrated track record of mining large, complex datasets for inconsistencies. - Hands-on experience at real scale: multi-table environments with frequent update cycles and complex ETL, not static extracts. - Proven ability to design data quality test plans that hold up when evaluated in aggregate. - Comfort making defensible judgment calls when the correct answer is genuinely unclear. - Track record of direct collaboration with Engineering and Data Science functions. - Cross-functional project management experience with accountability for business outcomes. - Customer-facing experience translating client feedback into technically feasible solutions.

Nice to have

- Managing data vendors, including selection, negotiation, and issue resolution. - Mentoring teammates on data operations best practices. - Background in data-as-a-product, market intelligence, or syndicated data products. - Prior experience in a small, remote-first team environment.

Technical environment

The stack centers on advanced SQL, DBT, YAML, and regex for data work, with BigQuery and GCP services such as Cloud Storage and Dataproc supporting complex ETL pipelines. BI tooling includes Looker and Redash, with git for version control, plus Python and exposure to pandas or PySpark for working with panel, longitudinal, or subscription-style datasets.