Inside the Role

The Engineering Quality, Safety and Compliance (EQSC) team at DTNA is at the heart of product integrity, design risk assessment, and data-driven quality improvement across vehicle development programs. EQSC works with design engineering, product validation, manufacturing, service, warranty, and cross-functional teams to improve how engineering quality work products are created, connected, governed, and used to support stronger design decisions.

In this role, you will be a key contributor to the EQSC team with responsibility for improving data lifecycle practices and connecting design risk assessment with manufacturing and real-world field insights. You will support short- and long-term quality improvement strategies, translate defined AI-enabled EQSC concepts into usable workflows, and help deploy practical data and process solutions through reliable, well-governed data models and agents.

This role is highly collaborative and requires the ability to work across Vehicle Level Engineering, Product Engineering, Product Validation, Manufacturing, Service, Quality, IT, and business stakeholders. The role will help operationalize the AI-enabled strategy envisioned by the leadership team across EQSC by coordinating user feedback, adoption documentation, training support, configuration inputs, and implementation readiness while preserving engineering verification ownership within the expert teams.

Posting Information

We provide a scheduled posting end date to assist our candidates with their application planning. While this date reflects our latest plans, it is subject to change, and postings may be extended or removed earlier than expected.

We Take Care of Our Team

Position offers a startingsalary range of$71,000 to $91,000USD

Pay offered dependent on knowledge, skills, andexperience

Benefits include 401kcompany contribution with company match up to8% as well as non-elective company contribution of3 - 7% depending on age;starting at 4 weeks paidvacation; 13+ calendar holidays;8 weekspaidparental leave; employee assistance program;comprehensive healthcare plans and wellnessprograms; onsite fitness (at some locations); tuitionassistance and volunteer paid time off; short-termand long-term disability plans.

What You Drive at DTNA

Support governed data pipelines, including Snowflake-enabled datasets, by helping prepare, clean, validate, and connect requirements, specifications, validation records, vehicle compliance inputs, defect investigations, manufacturing data, service data, warranty information, and field-quality insights.

Assist with SQL queries, data models, metadata fields, and data-quality checks that improve traceability, reliability, and readiness for analytics and AI-assisted workflows.

Contribute to AI-agent implementation by helping configure workflows, retrieval patterns, prompt examples, test cases, and deployment-support materials under guidance from senior team members.

Prepare approved standards, process guidance, historical examples, compliance references, investigation learnings, and engineering knowledge content for use in AI-assisted workflows and evaluation datasets.

Help test, validate, and deploy AI-agent capabilities using approved enterprise platforms, Snowflake-enabled data assets, Microsoft 365 Copilot / Copilot Studio, APIs, and related tools.

Capture data-quality issues, manual handoffs, duplicated steps, user pain points, pilot feedback, and improvement ideas in issue-tracking or backlog tools to support practical workflow improvements.

Support analysis of connected engineering, compliance, investigation, manufacturing, service, warranty, and field data to help improve risk assessment, product-quality decisions, corrective-action follow-up, and service diagnostics.

Help measure AI-agent output quality, efficiency, token usage, user feedback, and accuracy by supporting evaluation datasets, regression testing, grounding checks, stress testing, and hallucination-reduction reviews.

Create and maintain implementation notes, prompt/configuration change logs, user guidance, training aids, data definitions, known limitations, and adoption content in Confluence, SharePoint, and similar enterprise knowledge platforms.

Work with Vehicle Engineering, Product Engineering, Vehicle Compliance, Product Validation, Manufacturing, Service, Quality, IT, defect investigation teams, and regional/global stakeholders to support user acceptance testing, adoption, and well-governed AI and data solutions.

Knowledge You Should Bring

A bachelor27s degree in engineering, computer science, data science, or a related technical field.

02 years of relevant experience through work, internships, co-ops, academic projects, or applied technical projects.

Foundational understanding of AI/ML and GenAI concepts, including large language models, embeddings, retrieval, prompt patterns, and basic model evaluation.

Awareness of responsible AI practices, including grounding, hallucination reduction, privacy, access control, bias awareness, and human review for high-impact engineering decisions.

Basic experience preparing, cleaning, validating, joining, and documenting datasets for analytics, automation, or AI-assisted workflows.

Working knowledge of SQL, Python, REST APIs, and enterprise data-platform concepts, including Snowflake or similar environments.

Familiarity with basic software-development practices such as version control, configuration tracking, code review, testing discipline, and clear technical documentation.

Evaluation and regression-testing mindset, including the ability to create test cases, compare expected and actual results, document limitations, and support issue resolution.

Familiarity with collaboration, documentation, and issue-tracking tools such as Jira, Azure DevOps, Confluence, SharePoint, or similar platforms.

Basic awareness of automotive, engineering quality, product development, compliance, manufacturing, warranty, service, or field-quality workflows.

Ability to communicate clearly, collaborate across functions, learn quickly, ask good questions, and manage multiple tasks with guidance.

Exceptional Candidates Might Have

Applied project, internship, co-op, capstone, or portfolio experience that shows the ability to turn data or AI concepts into a working prototype, workflow, dashboard, or documented solution.

Hands-on exposure to AI-enabled workflows, custom AI agents, retrieval-augmented generation, vector search, embeddings, prompt engineering, or agent evaluation through coursework, projects, internships, or prototypes.

Practical experience using enterprise data platforms or business systems such as Snowflake, Dataverse, SAP, SharePoint, Power Platform, or similar environments to query, organize, connect, or visualize data.

Experience building simple Power BI, Excel, Python, or similar dashboards/reports to summarize usage, quality, adoption, workflow status, or data-quality metrics.

Exposure to automotive, manufacturing, warranty, service, aftermarket, vehicle compliance, defect investigation, or field-quality data and how those signals can support product-quality decisions.

Experience documenting requirements, test results, defects, user feedback, known limitations, or adoption materials in tools such as Jira, Azure DevOps, Confluence, SharePoint, or similar platforms.

Exposure to structured problem-solving, quality improvement, or engineering root-cause analysis methods

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Where We Work

This position is open to applicants who can work in (or relocate to) the following location(s)-

Portland, OR US. Relocation assistance is not available for this position.

Schedule Type:

Hybrid (4 days per week in-office / 1 day remote). This schedule builds our #OneTeamBestTeam culture, provides an unparalleled customer experience, and creates innovative solutions through in-person collaboration.