Senior Software Engineer — Python | Data Governance and Analytics
Role Overview
We are seeking a Senior Software Engineer with deep technical expertise in Python and Data Governance & Analytics (DGA) systems.
In this role, you will design, build, and scale high-performance data pipelines, analytics infrastructure, and governance frameworks that empower our engineering and product teams.
You will work closely with cross-functional architecture and DevOps teams to drive automated data ingestion, ensure stringent data quality, and build reliable backend services that support critical operational metrics and reporting.
Key Skills for This Role
Full Job Posting
Work Schedule
Standard (Mon-Fri)
Environmental Conditions
Office
Job Description
Experience Level: 6–9 Years
Team: Data Governance and Analytics Organization: Data, AI, and Visualization (DAIV), Global Software R&D
About the team
The Data Governance and Analytics Team is part of Data, AI, and Visualization (DAIV) within our global software R&D organization. We are a diverse, distributed team with colleagues across France and the Netherlands.
We bring digital technology into our products, solutions, and services, enabling data extraction, analysis, and connectivity. We develop software that supports data governance by managing data definitions, improving data quality, and making data more usable and useful. Our work prepares the organization for the age of AI, supports data-driven design, and enables value creation from data.
About the role
We are seeking a Senior Software Engineer with deep technical expertise in Python and Data Governance & Analytics (DGA) systems. In this role, you will design, build, and scale high-performance data pipelines, analytics infrastructure, and governance frameworks that empower our engineering and product teams. You will work closely with cross-functional architecture and DevOps teams to drive automated data ingestion, ensure stringent data quality, and build reliable backend services that support critical operational metrics and reporting.
Key Responsibilities
- Design, develop, test, and scaling of robust data ingestion pipelines, data models, and analytics backends using Python.
- Implement and automate data governance standards, metadata tracking, and data lineage mechanisms to ensure high data integrity, security, and compliance.
- Build capabilities supporting data extraction, processing, analysis, connectivity, and governance.
- Implement data validation and transformation logic to improve consistency, quality, and usability.
- Develop clear interfaces that enable other teams to integrate and reuse Python components.
- Identify opportunities to reuse existing software and simplify implementation across projects.
- Structure Python code into maintainable modules and packages with appropriate dependency management.
- Collaborate with architects to define component interfaces, dependencies, and integration approaches.
- Write automated tests and maintain reliable test coverage for critical functionality.
- Participate in code reviews and improve code readability, reliability, and maintainability.
- Investigate defects, troubleshoot software and integration issues, and improve performance where. needed.
- Maintain technical documentation and clear runbooks covering component interfaces, configuration, integration dependencies, and usage.
- Collaborate with software architects and engineers across the wider R&D organization.
- Establish strong reliability practices (logging/monitoring, retries/backoff, idempotency, concurrency controls, safe cleanup, traceability).
- Mentor engineers through code reviews, best practices, and technical leadership.
- Work effectively within an Agile Scrum / SAFe framework, participating in backlog refinement, sprint planning, technical estimation, PI planning, reviews, retrospectives, and cross-team coordination to support predictable and high-quality delivery.
Required Skills
- Expert-level proficiency in Python (async programming, data structures, performance optimization, and modular architecture).
- Experience developing shared Python packages or libraries used by other engineering teams.
- Ability to independently develop, test, debug, and maintain Python modules and libraries.
- Understanding of Python packaging, dependency management, error handling, and automated testing.
- Experience manipulating data, including cleaning, transforming, validating, or aggregating datasets.
- Knowledge of data analytics concepts.
· Strong foundation in SQL, relational/non-relational data modelling, and data transformation frameworks.
· Experience with data governance principles (data quality frameworks, cataloguing, lineage, and metadata management).
- Ability to collaborate effectively with colleagues across locations and disciplines.
- Ability to design modular, reusable, and maintainable software.
- Experience working in an agile DevOps environment.
- Experience using GitLab or GitHub for source control and collaborative development.
- Familiarity with build pipelines, test automation, and automated deployment.
- Understanding of containerization concepts, with familiarity with technologies such as Docker, K8s, Helm, etc
- Working knowledge of Agile delivery frameworks, including Scrum and SAFe, with experience operating effectively in sprint-based and cross-team planning environments.
- Strong communication and stakeholder management skills, with the ability to work effectively across global, cross-functional teams.
- Experience operating in structured product-development environments with strong quality, compliance, and delivery expectations.
Preferred Qualifications
- Bachelor’s or master’s degree, preferably in a technical or scientific discipline.
- 6+ years of software engineering experience with a strong focus on backend development, data engineering, or analytics platforms.
About Thermo Fisher Scientific
Thermo Fisher Scientific supplies instruments, laboratory products, life sciences solutions and pharmaceutical services to customers in research, healthcare, industry and applied markets. The company was formed in 2006 through the merger of Thermo Electron and Fisher Scientific.
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