Data Engineering at Thermo Fisher Scientific Inc.
Roles, Comp & Culture
An L6 staff data engineer at Thermo Fisher Scientific Inc. sits around $172K total comp from 21 verified salary datapoints. The primary Data Engineering tech consists of AWS, Spark and Kafka, according to current job listings. Thermo Fisher Scientific Inc. pays data engineers below other Technology companies. Reviews put them at 3.5 on Glassdoor, toward the bottom of the pack. Employee sentiment at Thermo Fisher Scientific Inc. reads neutral and employee happiness is trending down over the past year. Layoff risk scores low for the next 30 days. 8 data engineering roles are open right now.
Thermo Fisher Scientific Inc. data engineer compensation
Each level's figure is the median of individual Thermo Fisher Scientific Inc. offers at that level, so it reflects a typical outcome rather than an average pulled up by a few large packages. Total comp counts base salary plus equity and bonus annualized over the vest, and the range shown is the middle half of offers, with the top and bottom quarters trimmed off.
Thermo Fisher Scientific Inc. employee sentiment, tracked weekly
Employee happiness for data engineers over the past year, so you can see which direction it is moving, not just where it sits today.
Recent Thermo Fisher Scientific Inc. events
Layoffs, leadership changes, and other major moves at the company, with dates.
- LayoffMar 2026~173 roles cut
- Exec departureJan 2026Leadership change
- Exec departureJul 2025Leadership change
- Exec departureMay 2025Leadership change
- Exec departureFeb 2025Leadership change
Notable company events we track, with dates.
Thermo Fisher Scientific Inc. data engineering tech stack
The languages, storage, and processing tools Thermo Fisher Scientific Inc. data engineers actually work with, grouped by what they do. Tailor your system-design answers to this stack.
SQLThermo Fisher Scientific Inc. data engineer job openings
A live read on what they are hiring: open roles, recent postings, where, and at what level.
As a Customer Data Engineer, you'll help advance our mission of making the world healthier, cleaner and safer through strategic marketing initiatives.
Design, develop, and maintain scalable data pipelines and ETL processes integrating data from multiple sources and systems.
Build and optimize data architectures that support analytics, reporting, AI applications, and knowledge management initiatives.
Build pipelines that ingest, persist, cleanse and validate process and telemetry data.
Implement standard operating procedures, facilitate review sessions with functional owners and end-user representatives, and leverage technical knowledge and expertise to drive improvements.
Design, develop, and maintain data ingestion, data processing, and data pipelines according to best practices
Whether developing cloud applications, creating data pipelines, or building AI-powered features, you'll help shape the future of scientific technology while growing your career in serving science.
Practice for the Thermo Fisher Scientific Inc. loop
Round by round, the problems our model predicts for this company's interview. Rehearse the shapes their panels keep returning to.
Preparing for the Thermo Fisher Scientific Inc. loop
The round-by-round process, example questions, and prep plan are on the interview guide.
Compare Thermo Fisher Scientific Inc. with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at Thermo Fisher Scientific Inc. interview difficulty
- 01
Reading a solution is not the same as writing one
Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you
- 02
76% of hiring managers reject on the coding task, not the resume
From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice
- 03
5 problem shapes cover 80% of data engineer loops
Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition