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Python Software Engineer

Virtusa — Germany · Posted ~22 hours ago

Skills

Python Data pipelines Tabular data File-based data processing Data validation Data transformation Software testing CI Packaging System design AI-assisted development tools

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Summary ✨ AI‑Generated

Join a team building focused Python tools for scientific and data-intensive workflows rather than traditional backend or frontend systems. You’ll work on data ingestion, metadata pipelines, file validation and de-identification, dataset preparation, lightweight management interfaces, CI, packaging, and test coverage. Strong Python skills, practical data engineering ability, and sound software design judgment are essential. Experience working effectively with AI-assisted development tools is a valuable advantage.

Highlights

Work on focused, deployable Python tools for scientific and data-oriented problems, with an emphasis on clean, testable, well-documented engineering. The role offers exposure to data pipelines, validation, transformation, CI, packaging, and testing, while encouraging effective use of modern AI-assisted development tools.

Description

Concretely, you’ll be working on things like: - Data ingestion and metadata pipelines for whole slide images - De-identification and integrity checking of WSI files - Dataset splitting and stratification tools - Lightweight review and management UIs - CI, packaging, and test coverage for the above This is not a backend or frontend role. There’s no microservice architecture to maintain. The problems are scientific and data-oriented; the solutions are focused, deployable Python tools. We integrate AI assistance heavily into our development workflow — from requirements exploration to code review. Comfort working alongside AI tooling is a plus; experience shaping how a team uses it is even better. What we’re looking for Essential: - Strong Python — you write clean, testable, well-documented code without being prompted - Comfort working with data: tabular data, file-based pipelines, validation, transformation - Engineering common sense: you can design a simple system, reason about trade-offs, and know when not to over-engineer Self-directed: given a requirement and context, you can run with it and ask the right questions Useful background - Image data or signal processing (library is secondary — OpenCV, scikit-image, PIL, anything) - SQL and data warehousing (Snowflake, DBT, or similar) - CI/CD pipelines and packaging (GitHub Actions, uv, Docker) - Lightweight UI work in Python (NiceGUI, Streamlit, Gradio, or similar) - Experience in a regulated or quality-managed environment (medical devices, clinical, GxP, ISO, automotive, aerospace — any domain where software quality is formally managed).