Data Engineer

About Verita AI Verita AI builds high-trust data pipelines that enable AI systems to understand real-world workflows across finance, analytics, and operations. We work with domain experts to help train and evaluate next-generation AI systems on how modern data infrastructure and analytics engineering function in practice. Our founding team includes alumni of Mercor, Hudson River Trading, Citadel, IDEO, Stanford, and Yale. We partner with world-class researchers and engineers at leading AI labs to advance the state of the art. Verita AI is a seed-stage company valued at $25 million, having raised $6 million led by Kindred Ventures. About the Role We are hiring experienced Data Engineering Experts to help train and evaluate AI systems on real-world analytics engineering and data infrastructure workflows. This work focuses heavily on modern data stack tooling, particularly dbt and Airflow, and requires individuals who can reason through complex data engineering scenarios with precision and clarity. You will help create, review, and evaluate realistic workflows spanning data transformation, orchestration, warehouse design, testing, and analytics engineering best practices. This is a high-focus, project-based engagement best suited for experienced practitioners who are comfortable working independently and communicating technical reasoning clearly. What You’ll Work On You may be asked to build, review, or evaluate scenarios involving: Pipelines & Transformations • ETL/ELT workflows • dbt model development • Incremental model logic and watermark handling • Structured output table generation Orchestration & Reliability • Airflow or Dagster DAG design • Workflow orchestration logic • Data quality monitoring • Test suite validation and debugging Warehouse & Analytics Engineering • Schema and data contract design • Query optimization and performance tradeoffs • Warehouse modeling across Snowflake, BigQuery, Redshift, or Databricks • Analytics-focused data architecture decisions AI Evaluation & Reasoning • Reviewing AI-generated technical outputs for correctness • Explaining engineering reasoning step-by-step • Converting workflows into structured evaluation tasks • Providing detailed feedback to improve model performance Requirements • 3+ years of professional experience in data engineering or analytics engineering • Strong experience with dbt and Airflow • Experience working with modern cloud warehouses such as Snowflake, BigQuery, Redshift, or Databricks • Familiarity with data quality testing and validation workflows • Comfortable reading and producing technical artifacts including DAGs, dbt models, schema docs, and test suites • Strong written communication skills and attention to detail • Able to work independently and maintain high-quality output Preferred backgrounds include: • Analytics Engineering • Data Infrastructure • Platform/Data Tooling • Business Intelligence Engineering • Data Platform teams at high-scale technology companies Engagement Details • Expected commitment: 20–40 hours per week • Engagement duration: approximately 2–3 weeks initially, with potential extensions based on project needs and performance • Immediate onboarding available for qualified candidates • Fully remote and asynchronous Compensation Compensation ranges from $90–$125/hour depending on experience, technical depth, and prior domain expertise. Strong contributors may receive expanded scope and longer-term opportunities based on quality and throughput.