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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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