Data Engineer | Remote

Crossing Hurdles; United States; On-site; USD 140,000 - 180,000; Full time

Job summary

Crossing Hurdles is seeking a Data Engineer to design and maintain scalable data pipelines for ingesting and transforming data from multiple sources. You will collaborate with AI researchers and data scientists to structure datasets for experimentation and model training.

Ideal candidates have strong Python/SQL skills, experience with Pandas and NumPy, and familiarity with PostgreSQL or MySQL, enabling robust data models and storage systems for large-scale datasets.

Qualifications

• Strong proficiency in Python and SQL.
• Experience designing and maintaining ETL/ELT pipelines.
• Experience with data manipulation libraries such as Pandas and NumPy.
• Experience working with structured and semi-structured datasets.
• Familiarity with PostgreSQL or MySQL.

Responsibilities

• Design, build, and maintain scalable data pipelines to ingest, process, and transform data from multiple sources.
• Collaborate with AI researchers and data scientists to structure and prepare datasets for experimentation and model training.
• Develop and maintain data models, schemas, and storage systems optimized for large-scale datasets.
• Write efficient SQL queries and Python scripts to extract, transform, and analyze data.
• Ensure data quality, integrity, and reliability across data pipelines and storage layers.
• Implement data validation, monitoring, and automation workflows that support iterative research cycles.

Skills

Python

SQL

ETL/ELT pipelines

Pandas

NumPy

Structured data

PostgreSQL

MySQL

Job description

Position

Data Engineer

Type

Full-time

Compensation

$140K - $180K/yr

Location

Remote

Role Responsibilities

• Design, build, and maintain scalable data pipelines to ingest, process, and transform data from multiple sources.
• Collaborate with AI researchers and data scientists to structure and prepare datasets for experimentation and model training.
• Develop and maintain data models, schemas, and storage systems optimized for large-scale datasets.
• Write efficient SQL queries and Python scripts to extract, transform, and analyze data.
• Ensure data quality, integrity, and reliability across data pipelines and storage layers.
• Implement data validation, monitoring, and automation workflows that support iterative research cycles.

Requirements

• Have strong proficiency in Python and SQL.
• Have experience designing and maintaining ETL / ELT pipelines.
• Have solid experience with data manipulation libraries such as Pandas and NumPy.
• Have experience working with structured and semi-structured datasets.
• Be familiar with relational databases such as PostgreSQL or MySQL.