Data Engineer

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.