Engineering Specialist III - D Shift

Seagate Technology•Bloomington, MN

1d•Onsite

About The Position

As a Wafer Process Engineering Technician, you play a critical role in supporting Seagate’s thin‑film head manufacturing. You will partner closely with process and sustaining engineers to control processes, evaluate tool performance, execute data‑driven troubleshooting, and ensure the highest quality of recording head wafers. This position requires strong analytical skills, comfort working with data, and the ability to apply structured problem‑solving in a fast‑paced production environment. This role supports a complex, high‑reliability operating environment where sustained mental focus, continuous learning, and sound judgment are required throughout extended shifts.

Requirements

Proficient with computers and Microsoft Office.
Ability to apply quantitative reasoning and mathematical concepts to problem solving and data analysis.
Able to extract, filter, and interpret tool data logs and KPIV datasets using software tools such as Excel, JMP, or internal Seagate platforms.
Strong written and verbal communication skills to ensure effective documentation and handoffs.
Comfortable navigating digital documentation, Microsoft Teams, SharePoint libraries, and structured workflows in a fast‑paced manufacturing environment.
Comfortable with basic statistical concepts, including data interpretation and variability.
Able to communicate effectively with individuals of varying experience levels.
Demonstrated ability to maintain focus, accuracy, and mental endurance during extended work periods.
Quick learner capable of acquiring new concepts through hands‑on training and job shadowing.
Consistently adheres to established guidelines and escalation criteria, prioritizing product and process integrity.
Must be able to work a night shift schedule of 6:30 p.m. CST – 6:30 a.m. CST, Friday through Sunday and every other Thursday.
Typically requires a minimum of 2–4 years of related experience.
Experience in thin‑film wafer manufacturing environments.
Understanding of wafer thin‑film processing from an engineering or manufacturing support perspective.
Strong communication skills and ability to work effectively in a team.
Experience using tool log viewers, SPC platforms, or engineering dashboards to interpret KPIV trends.
Ability to perform data manipulation (sorting, filtering, charting) to support troubleshooting and cycle‑time improvement.
Experience using data visualization or analysis tools such as Excel, JMP, Minitab, or equivalent.

Nice To Haves

AA/AAS degree and/or comparable experience with wafer production equipment.
Capability to learn new data systems quickly and adapt to evolving digital workflows.
Familiarity with technical concepts and structured problem‑solving in STEM environments.
Ability to perform first‑level analytical investigations before escalation.
Ability to recognize when a process is statistically in control but not capable and communicate implications clearly.
Validates that tool adjustments are supported by data rather than anecdotal evidence.

Responsibilities

Evaluate tool and wafer datasets using statistical reasoning (SPC charts, distributions, correlation patterns) to determine appropriate next steps.
Perform analytical review of qualification data using trend interpretation, slope/line‑fit reasoning, and simple statistical testing to verify process stability before adjusting.
Apply calculations including ratios, offsets, tolerance windows, and proportional adjustments when determining parameter changes.
Navigate tool software interfaces to input, modify, and verify parameter changes in alignment with process control requirements.
Distinguish between normal process variation and true assignable causes using critical thinking and historical baseline comparison.
Use structured problem‑solving techniques (cause/effect reasoning, elimination testing, logic trees) when troubleshooting suspect processes.
Evaluate parameter impacts using quantitative reasoning, including linear relationships, proportional adjustments, and tolerance‑based thresholds.
Use tool data logs, KPIV (Key Process Input Variable) datasets, and wafer parametric information to identify trends, isolate suspect parameters, and support evidence‑based problem solving.
Combine tool logs, wafer data, and historical patterns to differentiate between equipment‑driven and process‑driven issues before escalating to engineering.
Synthesize findings into evidence‑based recommendations for engineering, escalating only when analysis indicates non‑routine deviation.
Participate in cross‑functional teams focused on yield improvement, scrap reduction, and reaction plan effectiveness.
Execute established engineering rules and procedures for non‑conforming wafers.
Analyze rework and scrap data to determine root cause and corrective action.
Apply root‑cause analysis grounded in data patterns (trends, distributions, frequency analysis).
Distinguish between process‑induced, equipment‑induced, and material‑induced variation.
Follow established escalation guidelines and decision frameworks, including notifying Engineers when required.
Collaborate with production teams to analyze bottlenecks and recommend data‑driven solutions.
Resolve factory floor engineering issues, escalating to engineering shift support when necessary.
Provide training for new technicians.
Continuously learn additional manufacturing areas as experience is gained.
Identify and suggest improvements to process engineering documentation used to control and qualify tools.
Translate analytical findings into clear documentation that supports engineering decisions and shift‑to‑shift knowledge transfer.
Monitor special processes, make adjustments, and document results.
Support engineering staff with trials, experiments, and manual runs.
Function as the “eyes and ears” of the Sustaining organization and process engineering team.
Identify and escalate issues related to wafers, processes, systems, or documentation.
Ensure compliance with all health and safety policies and procedures.
Apply a growing body of technical and operational knowledge to perform tasks accurately and safely.
Respond to previously unseen situations that require real‑time analysis and judgment rather than reliance on fixed procedures.
Continuously update personal knowledge as tools, processes, and system behaviors evolve.
Recognize emerging issues early and take appropriate action through documentation, escalation, or collaboration.
Operate effectively under conditions of ambiguity while balancing precision and timely decision‑making.