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Job details

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more.

Role Description

This role involves designing resilient ETL/ELT pipelines and data contracts to ensure datasets are analytics- and ML-ready. You’ll validate, enrich, and serve data with strong schema and versioning discipline, building the backbone that powers AI research and production systems.

  • Build and maintain ETL/ELT pipelines with a focus on scalability and resilience.
  • Validate and enrich datasets to ensure they’re analytics- and ML-ready.
  • Manage schemas, versioning, and data contracts to maintain consistency.
  • Work with PostgreSQL/SQLite, Spark/Duck DB, and Airflow to manage workflows.
  • Optimize pipelines for performance and reliability using Python and pandas.
  • Collaborate with researchers and engineers to ensure data pipelines align with product and research needs.

Qualifications

  • Have a background in computer science, data engineering, or information systems.
  • Are proficient in Python, pandas, and SQL.
  • Have hands-on experience with databases like PostgreSQL or SQLite.
  • Understand distributed data processing with Spark or DuckDB.
  • Are experienced in orchestrating workflows with Airflow or similar tools.
  • Work comfortably with common formats like JSON, CSV, and Parquet.
  • Care about schema design, data contracts, and version control with Git.
  • Are passionate about building pipelines that enable reliable analytics and ML workflows.

Requirements

  • To design, validate, and maintain scalable ETL/ELT pipelines and data contracts that produce clean, reliable, and reproducible datasets for analytics and machine learning systems.

Benefits

  • You’ll be classified as an hourly contractor to Mercor.
  • Paid weekly via Stripe Connect, based on hours logged.
  • Part-time (20–30 hrs/week) with flexible hours—work from anywhere, on your schedule.
  • Weekly Bonus of $500–$1000 USD per 5 tasks.
  • Remote and flexible working style.
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