Senior Data Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 3h ago
The date the source published, not the day we noticed it (2026-09-15). Last seen at its source just now.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
LATAM
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $7,062–7,250/mo
Middle 50% of 18 listings that do state pay — Engineering · Senior · LATAM · USD/month. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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ashby employer's own board first seen 2h ago · last seen just now
The listing
Remote | LATAM | Full-time | B2B Contract
Our client is a growing technology company developing market intelligence and software solutions. They are looking for a Senior Data Engineer to join a small international team and take ownership of complex data engineering challenges involving large-scale data processing, standardization, matching, and data quality.
The role is highly hands-on and suits an engineer who is comfortable working independently, making technical decisions, and taking solutions from initial design through reliable production delivery.
What you’ll do
Design, build, and maintain scalable production data pipelines.
Ingest, process, and transform large volumes of data from multiple sources.
Develop solutions for data standardization, normalization, matching, and validation.
Build data quality controls and monitoring to identify malformed, inconsistent, or incorrect data.
Design reliable approaches to data corrections, updates, reprocessing, and backfills.
Improve the architecture, scalability, reliability, and performance of the data platform.
Take end-to-end ownership of technical solutions and production quality.
Work closely with a small engineering team while independently driving your area of responsibility.
Use AI-assisted engineering tools and practices to improve development efficiency.
What we’re looking for
4+ years of hands-on Data Engineering experience with production data systems.
Strong Python skills.
Hands-on experience with PySpark / Apache Spark and distributed data processing.
Strong experience building and maintaining ETL/ELT and data ingestion pipelines.
Experience working with large, complex datasets and multiple data sources.
Strong SQL and data modeling skills.
Experience with modern data platforms, data lakes, lakehouse, or similar architectures.
Strong understanding of data quality, validation, monitoring, and data reliability.
Ability to independently design solutions, troubleshoot production problems, and take ownership of delivery.
Fluent English.
Nice to have
Delta Lake experience.
MongoDB experience.
Databricks experience.
Experience with data matching, reconciliation, deduplication, or complex standardization problems.
Experience in a startup, scale-up, small product company, or lean engineering team.
Docker, Linux, CI/CD, or related DevOps experience.
Experience mentoring or technically supporting other engineers while remaining hands-on.
Active use of AI coding tools or agent-based development practices.
What we offer
100% remote work from LATAM.
Full-time B2B contract.
High level of technical ownership and autonomy.
Direct impact on architecture and product development.
Complex engineering challenges rather than narrowly defined implementation tasks.
Small, international team with direct communication and minimal bureaucracy.
Professional development and continuous learning opportunities.