Senior Data Engineer

Job Description

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 is $150/hour

Strong contributors may receive expanded scope and longer-term opportunities based on quality and throughput.

Hybrid remote