Remote | Data Engineer — $140,000–$180,000/year

Summary

24-MAG connects experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams. The company is seeking an experienced Data Engineer to build scalable ETL pipelines, transform structured and unstructured data, improve data quality, and support research, analytics, and AI/ML initiatives. The role also involves database engineering, exploratory data analysis, automation, documentation, and collaboration with researchers, data scientists, and engineers.

Responsibilities

• Design, develop, and maintain scalable ETL pipelines
• Build reliable workflows for data extraction, transformation, and loading
• Improve pipeline efficiency, maintainability, and scalability
• Automate recurring data-processing tasks
• Monitor pipelines and troubleshoot operational failures
• Collect data from structured and unstructured sources
• Clean, normalise, transform, and organise raw datasets
• Develop repeatable transformation workflows
• Identify inconsistencies and incomplete records
• Prepare reliable datasets for downstream research and analytics
• Conduct exploratory analysis across complex datasets
• Identify patterns, trends, anomalies, and quality issues
• Investigate unexpected behaviour in source data
• Produce analytical summaries supporting technical decisions
• Communicate findings clearly to interdisciplinary stakeholders
• Write and optimise SQL queries for data extraction and transformation
• Work with relational database systems such as PostgreSQL and MySQL
• Design and maintain database schemas
• Improve query performance and data-access patterns
• Support reliable and maintainable database workflows
• Develop logical and physical data models
• Define database structures appropriate to analytical and operational requirements
• Evaluate schema design and transformation strategies
• Support scalable data-storage solutions
• Maintain consistency across related datasets and systems
• Develop automated data-validation workflows
• Monitor accuracy, integrity, consistency, and completeness
• Identify and resolve data-quality issues
• Establish repeatable quality-control procedures
• Ensure datasets meet downstream analytical and modelling requirements
• Build data-processing workflows in Python
• Use Pandas and NumPy for transformation and analysis
• Develop reusable data-processing components
• Improve performance and reliability of analytical workflows
• Support automation of repetitive data-engineering processes
• Prepare datasets for AI and machine-learning initiatives
• Collaborate with data scientists and researchers on data requirements
• Support model-development workflows through reliable data preparation
• Evaluate data suitability for training and evaluation use cases
• Help structure data pipelines supporting AI/ML experimentation
• Automate recurring reporting and data-processing activities
• Build repeatable validation and monitoring processes
• Reduce manual intervention across routine data workflows
• Improve operational visibility into pipeline health
• Support timely delivery of high-quality datasets
• Document pipelines, schemas, workflows, and technical decisions
• Maintain clear operational and development documentation
• Investigate and resolve pipeline failures
• Diagnose data-related technical issues
• Communicate root causes and remediation steps clearly

Skills

• Strong proficiency in Python
• Strong proficiency in SQL
• Hands-on experience designing and maintaining ETL pipelines
• Experience conducting exploratory data analysis
• Proficiency with Pandas and NumPy
• Experience with PostgreSQL and MySQL
• Strong understanding of data modelling and database schemas
• Experience working with structured and unstructured datasets
• Demonstrated ability to maintain data quality, integrity, and reliability
• Familiarity with Jupyter Notebook, VS Code, PyCharm, or comparable development environments
• Strong analytical and problem-solving skills
• Strong written and verbal communication skills
• Experience collaborating with researchers, data scientists, or engineering teams is valuable
• Exposure to AI and machine-learning workflows is advantageous
• Familiarity with scikit-learn is beneficial
• Experience with Hugging Face Transformers is a plus
• Familiarity with AI APIs or comparable AI platforms is advantageous
• Experience preparing datasets for AI/ML model development is highly valuable

Qualifications

Must Haves

• Strong proficiency in Python
• Strong proficiency in SQL
• Hands-on experience designing and maintaining ETL pipelines
• Experience conducting exploratory data analysis
• Proficiency with Pandas and NumPy
• Experience with PostgreSQL and MySQL
• Strong understanding of data modelling and database schemas
• Experience working with structured and unstructured datasets
• Demonstrated ability to maintain data quality, integrity, and reliability
• Familiarity with Jupyter Notebook, VS Code, PyCharm, or comparable development environments
• Strong analytical and problem-solving skills
• Strong written and verbal communication skills

Nice to Haves

• Experience collaborating with researchers, data scientists, or engineering teams is valuable
• Exposure to AI and machine-learning workflows is advantageous
• Familiarity with scikit-learn is beneficial
• Experience with Hugging Face Transformers is a plus
• Familiarity with AI APIs or comparable AI platforms is advantageous
• Experience preparing datasets for AI/ML model development is highly valuable

Benefits

• Full-time engagement
• Fully remote