Senior Data Engineer

OnHires

. Design, build, and maintain scalable production data pipelines .

Posted 9/15/2026full-timeRemoteSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining scalable data pipelines, with a strong focus on data quality, validation, and reliability. Proficient in Python, PySpark, and SQL, with hands-on experience in modern data architectures and ETL/ELT processes.

Highest-signal resume keywords

Data Engineering ExperiencePython ProgrammingETL/ELT Pipeline DevelopmentSQL and Data ModelingData Quality and Validation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringPythonPySparkETLELTSQLData ModelingData QualityData ValidationData Reliability

Soft Skills

Problem SolvingOwnershipIndependenceCollaboration

Tools & Technologies

Apache SparkData LakesDelta LakeMongoDBDatabricksDockerLinuxCI/CDAI Coding Tools

Industry Keywords

Data PipelineData ProcessingData StandardizationData MonitoringLean Engineering

Tech Stack

Tools & technologies

ApacheDockerETLLinuxMongoDBPySparkPythonSparkSQL

About the role

Key responsibilities & impact

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 the assigned area of responsibility
Use AI-assisted engineering tools and practices to improve development efficiency

Requirements

What you’ll need

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, MongoDB, Databricks, data matching, reconciliation, deduplication, complex standardization, startup/scale-up or lean engineering team experience, Docker, Linux, CI/CD, mentoring, and AI coding tools or agent-based development practices

Benefits

Comp & perks

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