Data Engineer – AI Model Training - The United States

If you’re a senior Data Engineer who thrives on precision, systems thinking, and building reliable data foundations, this is a unique opportunity to contribute directly to how the next generation of AI systems reason about data infrastructure, pipelines, and analytics workflows. We’re looking for experienced Data Engineers who understand modern data stacks, ETL/ELT architecture, orchestration, data modeling, warehouse design, quality validation, governance, and production-scale reliability.Your work will help strengthen how AI models reason through complex data engineering scenarios, identify technical errors, and communicate implementation guidance clearly.

### Your Profile
- 4+ years of professional experience in data engineering, with significant hands-on work designing, building, and maintaining production-grade data pipelines.
- Deep knowledge of SQL, data modeling, ETL/ELT architecture, orchestration frameworks, warehouse/lakehouse patterns, and modern data stack tools such as dbt, Airflow, Snowflake, BigQuery, Databricks, Fivetran, or similar platforms.
- Strong understanding of distributed data systems, batch and streaming workflows, schema design, data validation, data observability, lineage, and pipeline reliability.
- Proven experience optimizing complex SQL queries, troubleshooting data quality issues, designing scalable transformations, and supporting analytics or machine learning-ready datasets.
- Demonstrated experience in translating ambiguous business or technical requirements into reliable data models, pipeline designs, and implementation plans.
- Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Statistics, Engineering, or a related technical field; equivalent professional experience will also be considered.
- Previous experience with AI data training, annotation, or evaluating AI-generated technical content is a strong plus.

### Key Responsibilities
- Evaluate AI-generated answers to data engineering prompts for technical accuracy, completeness, clarity, and real-world feasibility.
- Challenge advanced language models with complex Data Engineer scenarios involving SQL, Python, ETL/ELT design, orchestration, warehousing, data modeling, and pipeline reliability.
- Review and refine AI-generated prompts, responses, rubrics, and reference answers to ensure they reflect senior-level data engineering judgment.
- Provide structured feedback that identifies incorrect assumptions, missing constraints, weak reasoning, inefficient implementations, or unsafe recommendations.
- Shape AI communication standards by helping models explain data architecture, debugging steps, tradeoffs, and implementation patterns clearly and responsibly.
- Support benchmarking efforts by evaluating model performance across realistic data engineering workflows, edge cases, and failure modes.
- Develop and review high-quality examples that demonstrate strong reasoning around pipeline design, data quality checks, data contracts, schema evolution, and system scalability.