Data Engineering and SQL Expert

Vraify; Deutschland; Remote; EUR 41,000 - 58,000; Part time

Benefits offered by this job

Remote project-based work

Job summary

Vraify is seeking contributors to help evaluate and craft realistic data engineering tasks. You will work with SQL, data modelling, ETL/ELT, and data warehouses, testing AI-generated code and agent traces for correctness and quality. Strong debugging and clear English communication are essential.

Remote project-based work is available, with compensation up to $40/hour depending on project complexity and demonstrated expertise. Prior portfolio or GitHub evidence is valuable for evaluation.

Qualifications

• Strong practical depth in SQL, data modelling, ETL/ELT, warehouses, streaming systems, data quality, and performance tuning.
• Ability to navigate unfamiliar code or specifications and communicate technical decisions in clear written English.
• Verifiable evidence such as meaningful GitHub work, shipped software, a competition profile, technical writing, or a portfolio.

Responsibilities

• Create, solve, or review realistic tasks involving SQL, data modelling, ETL/ELT, warehouses, streaming systems, data quality, and performance tuning.
• Test AI-generated code and agent traces for correctness, edge cases, regressions, unsafe assumptions, and incomplete fixes.
• Write strong tests, reference patches, expected outcomes, and concise explanations of the failure mode.
• Review peer-authored tasks for realism, reproducibility, difficulty, and clean evaluation criteria.

Skills

SQL

Data modelling

ETL/ELT

Warehouses

Streaming systems

Data quality

Performance tuning

English writing

Tools

GitHub

Job description

Help us find where advanced coding agents break in data engineering and sql. Build or solve realistic tasks, stress-test generated code, and explain failures with reproducible evidence. Remote project-based work can pay up to $40/hour, depending on the project and demonstrated expertise.

What you'll do:

• Create, solve, or review realistic tasks involving SQL, data modelling, ETL/ELT, warehouses, streaming systems, data quality, and performance tuning.
• Test AI-generated code and agent traces for correctness, edge cases, regressions, unsafe assumptions, and incomplete fixes.
• Write strong tests, reference patches, expected outcomes, and concise explanations of the failure mode.
• Review peer-authored tasks for realism, reproducibility, difficulty, and clean evaluation criteria.

What we look for:

• Strong practical depth in SQL, data modelling, ETL/ELT, warehouses, streaming systems, data quality, and performance tuning.
• Ability to navigate unfamiliar code or specifications and communicate technical decisions in clear written English.
• Verifiable evidence such as meaningful GitHub work, shipped software, a competition profile, technical writing, or a portfolio. Confidential industry work can be described in the application without exposing proprietary material.
• Prior AI-evaluation experience is useful but not required; strong engineering judgement and testing discipline matter more.