Data Analytics Engineer
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Before you apply
LocationLATAM
Work moderemote
Typefull-time
SenioritySenior
Experience6+ yrs
DepartmentAN
Company size51–200 people
First seenSep 10, 2026 · 1mo ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
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
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
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
Skills
SQL
DBT
YAML
Regex
Excel
BigQuery
GCP
Cloud Storage
Dataproc
ETL
Looker
Redash
git
Python
pandas
PySpark
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
Requirements
•
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