Metals DX Engineer

Samsung•Taylor, TX

6d•Onsite

About The Position

The main focus of the DX Engineer in the Metals Department at Samsung Austin Semiconductor, is to update how we handle data and tools on the fab floor. They build and maintain the data ingestion pipelines and interaction tools that the department relies on, making sure everything is secure, stable, and runs with minimal manual intervention. In addition to system maintenance, a big part of their role is working directly with equipment and process engineers to find where improvements can be made. They will take those pain points and turn them into automated tools that help us catch quality issues faster and improve overall yield. We collect millions of data points every second, and part of the DX engineer's job is making sense of that data and wrangling it into usable form for process and equipment owners.

Requirements

BS or MS in Computer Science or Engineering.
Python (pandas, NumPy, FastAPI, streamlit).
SQL & modern data-warehouse concepts.
CI/CD tools (GitHub Actions, GitLab CI).
Experience designing scalable application schemas and systems.
Data-security & compliance basics.
Strong multitasking and project‑management abilities.
Problem-solving & analytical thinking.
Agile/Scrum Collaboration.
Communication- ability to translate technical concepts for non-technical stakeholders.
Continuous learning mindset.
Time-management & prioritization.

Nice To Haves

Web app development experience (Vue.js, Javascript, HTML/CSS) is a plus.

Responsibilities

Manage health of all digital tools (dashboards, apps, Jarvis scripts).
Gather Voice‑of‑the‑Customer (VOC) feedback for new features and enhancements.
Implement automated alerting and logging.
Deliver full‑stack solutions, including: Database- schema design, table creation, stored procedures, and SQL (PostgreSQL) / Web‑app - FastAPI, Python, Vue.js, Streamlit, JavaScript, HTML/CSS, jQuery.
Perform regular patching, dependency upgrades, and security hardening.
Write clean, modular, and documented code for data ingestion, transformation, and analytics.
Build reusable libraries and create scheduled scripts.
Prototype PoCs using rapid-iteration cycles (Design > Build > Test > Refine).
Conduct feasibility studies for AI/ML integration, low-code platforms, or RPA where appropriate.
Inter-departmental collaboration.
Maintain a central knowledge base.