Staff Engineer, Data

Location: Brno, Czechia

Category: Research & Development

Employment Type: Full time

Work Schedule

Standard (Mon-Fri)

Environmental Conditions

Office

Job Description

Do you have a passion for innovative ideas and groundbreaking discoveries? With over $1 billion invested annually in R&D, at Thermo Fisher Scientific you'll help to solve some of the world's toughest challenges, from giving cancer patients hope, ensuring safe drinking water and helping law enforcement address cases through forensics. We empower our teams to put science into meaningful action and give our R&D colleagues the autonomy, resources and tools they need to take science a step beyond.

Our group

At Sample Prep and Analysis group (SPA), we develop and maintain the software for automation of operation of Scanning Electron Microscopes. Electron microscopy is a high-tech industry with an outsized impact across fields, from semiconductors and materials science to life sciences and medicine. Our solution is crucial for semiconductor industry to identify errors in the production of semiconductor components and advance their research and production, to make them more robust, reliable, energy efficient. We see enormous potential in generative AI to reshape how this work is done, and the person we hire will be given the chance to become part of that revolution.

As a member of an international team of scientists, software developers, and engineers, you'll own the data foundation the rest of the team builds on. Where our analysts focus on what the data means, you make that work possible by designing the platform that ingests, stores and serves our process and telemetry data reliably, and making it ready for both people and AI agents to use.

Daily Challenges

- Build pipelines that ingest, persist, cleanse and validate process and telemetry data.

- Enable data-driven decisions to advance development of high-tech machines for the semiconductor industry.

- Design a well-governed platform with uniform access, data quality, lineage and observability by default.

- Make the department AI-ready, so agentic systems can reach our data through clean, well-described interfaces.

- Work mostly on-premise on Linux, while designing for portability so we stay hosting-agnostic.

Knowledge, Skills, Abilities

This is a senior role, but you don't need to tick every box. Bring the fundamentals and the curiosity, and we'll grow the rest together. You should have:

- A collaborative mindset built on open communication, trust and shared responsibility

- A university degree in computer science, software engineering or a related field

- At least three years of professional experience in data engineering

- A solid grasp of the fundamentals: data modeling, quality, orchestration and lineage

- Comfort with Linux, shell scripting, SQL and a high-level language (preferably Python)

- Professional working proficiency in English

Bonus points, or things you'll be keen to learn here:

- Containers and orchestration: Docker, Kubernetes

- PostgreSQL and lakehouse table formats such as Iceberg or Delta

- Object stores for microscope-generated images, particularly MinIO

- Pipeline and streaming tools: Apache Spark, Apache NiFi, Airflow, Kafka

- Data governance: IAM, RBAC, cataloging, lineage and compliance

- AI engineering: embeddings, vector databases, RAG, MCP and agentic workflows

- Infrastructure as Code (Terraform) and observability (OpenTelemetry, Prometheus, Grafana, Loki)

Technologies we use

- Python 3, SQL, PostgreSQL

- Linux, shell scripting

- Docker, Kubernetes, Terraform

- Apache Spark, Apache NiFi, Kafka

- Iceberg / Delta lakehouse, MinIO object storage

- OpenTelemetry, Prometheus, Grafana, Loki

- GitHub, CI/CD, JIRA, Confluence

Our Mission is to enable our customers to make the world healthier, cleaner and safer. As one team of 100,000+ colleagues, we share a common set of values - Integrity, Intensity, Innovation and Involvement. #StartYourStory at Thermo Fisher Scientific, where diverse experiences, backgrounds and perspectives are valued.