Data Engineer | $4-$5/hr Remote
Overview
A full-time remote role for a data engineer who can build and maintain scalable ETL pipelines and handle both structured and unstructured data. You will transform raw information into reliable datasets that support research, analytics, and AI model work, with exposure to machine learning tools viewed as valuable. You’ll collaborate with researchers, data scientists, and engineers to prep data for AI initiatives, and you’ll work with familiar tools and environments to ensure solid data quality.
What You'll Do10
- 1Design, implement, and maintain scalable ETL processes that move data from multiple sources into usable structures.
- 2Ingest, clean, transform, and organize both structured and unstructured data for analytics and modeling.
- 3Perform exploratory data analysis to surface trends, patterns, anomalies, and data quality issues.
- 4Partner with researchers, data scientists, and engineers to curate datasets for AI and ML projects.
- 5Create and maintain data models, schemas, and storage solutions that support reliable data access.
- 6Develop optimized SQL queries to support data extraction, transformation, and analysis.
- 7Guard data quality, integrity, and security across pipelines and databases.
- 8Automate data validation, reporting, and recurring data workflows.
- 9Document pipelines, processing steps, and key technical decisions.
- 10Troubleshoot pipeline failures and resolve data-related issues.
Requirements5
- 1Strong Python and SQL skills with hands-on experience building ETL pipelines.
- 2Experience performing exploratory data analysis and working with Pandas and NumPy.
- 3Proficiency with relational databases, especially PostgreSQL and MySQL, and solid data modeling knowledge.
- 4Ability to handle both structured and unstructured datasets and ensure data quality and reliability.
- 5Familiarity with development tools such as Jupyter Notebook, VS Code, or PyCharm.
Who Should Apply
The ideal candidate has deep experience designing and maintaining ETL pipelines, plus a solid track record cleaning and organizing data for analytics and ML use cases. This role fits someone who can collaborate with researchers and data scientists to prepare datasets for AI initiatives. It’s less suitable for applicants who lack hands-on Python and SQL skills or who cannot work with both structured and unstructured data. Common gaps include limited experience with ETL workflows or insufficient exposure to ML-ready data preparation.
Salary Insight
Pay is not disclosed here; compensation discussions occur later in the process.
Location
Required Skills
Application Tip
Highlight a concrete ETL project where you built a scalable pipeline, including data quality checks and how you validated results using SQL and Python analytics.
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How your application is processed
1Application received
Your resume and details are logged the moment you apply.
2ATS + eligibility screening
We check your profile against the role’s skills, seniority, and requirements.
3Employer sees qualified profiles only
Only candidates who clear screening move forward.
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