Data Analytics Engineer

Responsibilities

Data Quality

• Monitor data quality at each stage of the pipeline and build scalable test plans that validate data in aggregate
• Distinguish real signal from data-quality issues using hypothesis testing to rule out sources of discrepancy
• Run root-cause investigations that end in prevention, not just a fix

Release & Project Management

• Own the end-to-end data product release: accurate, reliable, on-time
• Project-manage cross-functional squads across multiple concurrent deadlines

Communicate action plans and timelines directly to clients

• Take DRI ownership of coverage expansion initiatives, from scoping to delivery
• Partner with Commercial to turn how customers use the data into frictionless solutions
• Work with Data Science and Engineering to close feasibility gaps
• Use AI to streamline workflows without adding QA overhead

Preferred

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

About the Role

We 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.

Data

Advanced SQL, DBT, YAML, Regex, Excel

Warehouse & Cloud

BigQuery, GCP (Cloud Storage, Dataproc), complex ETL

BI

Looker, Redash, git

Plus

Python, panel/longitudinal/subscription data, pandas or PySpark exposure

Required Qualifications

6+ years in data-focused roles

• Expert SQL, with a track record of mining large datasets for inconsistencies
• Experience at real scale: complex multi-table environments with frequent update cycles, not static extracts
• Experience designing data quality test plans that hold up in aggregate
• Comfort making defensible judgment calls when the outcome is genuinely unclear

Direct collaboration with Engineering and Data Science

• Cross-functional project management with accountability for business outcomes
• Customer-facing experience translating client feedback into technical feasibility