About DevSavant
DevSavant 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 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.
Key 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
Product & Collaboration
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
Core Technical Stack
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
Nice to Have
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