> Markdown version of [/jobs/ext/4525195-ai-data-data-engineer](https://www.wearedevelopers.com/jobs/ext/4525195-ai-data-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI & Data - Data Engineer - **Company:** Artech - **Location:** Seattle, WA, United States - **Salary:** $128,960.0 - $135,200.0 - **Contract:** Permanent contract - **Skills:** Unity 3d, Artificial Intelligence, Software as a Service, Databases, Data Governance, Extract Transform Load (ETL), Python (Programming Language), Knowledge Management, Performance Tuning, Salesforce.Com, SQL Databases, Data Processing, Scripting, Snowflake, Pyspark, Data Pipelines, Workday, Cisco, Databricks - **Published:** November 2, 2026 - **Apply:** https://www.careerbuilder.com/job-details/ai-data-data-engineer-seattle-wa--62abb272-4e8f-4dc8-930e-753f673ff148 ## About the Role Artificial Intelligence (AI), Artificial Intelligence (AI), Performance Tuning/Optimization, SQL (Structured Query Language), Cisco Unity, Database Extract Transform and Load (ETL), Software as a Service (SaaS), Salesforce.com, Python Programming/Scripting Language, Data Quality, * Hands-on experience with Databricks (PySpark/SQL, Unity Catalog), DBT (models, tests, docs), and Snowflake (SQL, performance tuning). * Strong background in ETL/ELT pipeline development. * Experience with enterprise SaaS systems (Salesforce, D365, Coupa, Workday, Concur). * Solid SQL and Python skills; knowledge of data quality frameworks. * Prior work experience at client or in client's Industry. * Applicants must be able to work directly for Artech on W2., * Experience in designing reusable data models for AI/ML. * Familiarity with data governance standards and cost efficiency strategies. * Strong collaboration skills with AI/Data Science teams. ## Description * Ingest and process data from various enterprise and external sources. * Build and maintain data pipelines in Databricks, DBT, and Snowflake. * Implement data quality tests, documentation, and lineage in DBT. * Collaborate with Knowledge Management teams to provide feature-ready datasets.