Data Engineer – Remote

About the role

We are seeking a senior Data Engineer to work remotely on a contract basis. You will help shape how next‑generation AI systems understand and reason about data infrastructure, pipelines, and analytics workflows. The role blends hands‑on data engineering with evaluating AI‑generated technical content.

Key responsibilities

• Evaluate AI‑generated answers to data‑engineering prompts for technical accuracy, completeness, and feasibility.
• Challenge large language models with complex scenarios involving SQL, Python, ETL/ELT design, orchestration, warehousing, and pipeline reliability.
• Review and refine AI prompts, responses, rubrics, and reference answers to reflect senior‑level judgment.
• Provide structured feedback on incorrect assumptions, missing constraints, weak reasoning, or unsafe recommendations.
• Help define AI communication standards for explaining data architecture, debugging steps, trade‑offs, and implementation patterns.
• Support benchmarking by evaluating model performance across realistic data‑engineering workflows and edge cases.
• Develop high‑quality examples that demonstrate strong reasoning around pipeline design, data quality checks, data contracts, and schema evolution.

Required profile

• 4+ years of professional data‑engineering experience building production‑grade pipelines.
• Deep knowledge of SQL, data modeling, ETL/ELT architecture, and modern data‑stack tools (dbt, Airflow, Snowflake, BigQuery, Databricks, Fivetran, etc.).
• Strong understanding of distributed data systems, batch and streaming workflows, schema design, data validation, observability, lineage, and reliability.
• Proven ability to optimise complex SQL queries, troubleshoot data‑quality issues, and design scalable transformations for analytics or ML‑ready datasets.
• Bachelor’s degree in a technical field or equivalent professional experience; experience with AI data training or annotation is a plus.

Required skills

• SQL
• Python
• dbt
• Apache Airflow
• Snowflake
• Google BigQuery
• Databricks
• Fivetran
• Data modeling
• ETL/ELT processes
• Data warehousing / lakehouse patterns
• Distributed data systems
• Batch and streaming processing
• Data validation and observability
• Data lineage and pipeline reliability