Remote Data Engineer II: AI & Data Quality Focus

Russell Tobin; Mountain View (CA); Remote; USD 83,000 - 90,000; Full time

Job summary

Russell Tobin in Mountain View, CA is seeking a Data Engineer II for a 6-month contract (extendable). The role is 100% remote (PST hours) with a W2 DOE rate of $60-$65/hour.

You will work hands-on with Databricks and advanced SQL to profile tables, validate grain/join, and contribute to data foundations. Responsibilities include partnering with data science stakeholders, translating requirements into a backlog, designing schemas and data models, building proofs of concept, and advancing data

Qualifications

• 4-8 years in analytics engineering, data engineering, or an adjacent data role.
• Ability to turn an under-specified request into a clear, buildable requirement.
• Experience designing data models and schemas for analytical use.
• Hands-on Databricks and advanced SQL; Git and Python a plus.
• Fluency using AI tools for research and cross-team partnerships.

Responsibilities

• Partner with data science stakeholders to understand the business problem and data needed.
• Translate requirements into a backlog and maintain clear specifications for data engineering.
• Design schemas, tables, and data solutions meeting trust and documentation standards.
• Build proofs of concept from design documents to notebooks validated against real data.
• Collaborate with source data owners and data engineering through build and UAT.
• Advance data foundations: observability, schema monitoring, quality rules, data contracts; support AI workflows.

Skills

Databricks

Advanced SQL

Python

Git

AI tools

Tools

Databricks

Git

Job description

Russell Tobin in Mountain View, CA is seeking a Data Engineer II for a 6-month contract (extendable). The role is 100% remote (PST hours) with a W2 DOE rate of $60-$65/hour.

You will work hands-on with Databricks and advanced SQL to profile tables, validate grain/join, and contribute to data foundations. Responsibilities include partnering with data science stakeholders, translating requirements into a backlog, designing schemas and data models, building proofs of concept, and advancing data