Senior Python Software Engineer (Production Data & Model Services)

Description

We are seeking a Senior Python Software Engineer (Production Data & Model Services) to design, build and operate production-grade Python applications and data pipelines. In this role, you will transform quantitative and data science prototypes into robust, deployable services while collaborating closely with platform teams to ensure scalable and well-governed solutions. Responsibilities Build and run production-grade Python applications including APIs and batch jobs with strong SDLC practices covering code reviews, testing, CI/CD, observability and documentation Develop robust batch and near-real-time data pipelines reading and writing governed storage with Parquet and columnar formats following approved patterns Transform quant and data science prototypes into deployable packages and services that are typed, modular and versioned Expose scoring and analytics capabilities via APIs or scheduled jobs rather than notebook-only deliverables Collaborate with platform teams on Databricks and Spark connectivity Optimize PySpark workloads when needed to ensure performance and scalability Maintain release discipline through Git workflows, automated tests and code reviews Operate within governed platform environments to ensure compliance and reliability Requirements 3+ years of experience in Python engineering with proficiency in packaging (wheels/pyproject), typing, clean architecture, error handling and performance mindset Proven background in production SDLC including Git workflows, automated tests, CI/CD, code reviews and release discipline Strong skills in Pandas and NumPy applied to production pipelines Familiarity with data formats such as Parquet and governed data access patterns Experience building and operating APIs and services using FastAPI, Flask or similar frameworks Competency in working within governed platform environments such as Databricks or containerized dev platforms Nice to have Skills in scikit-learn for production feature and scoring pipelines, including reproducible transforms and model packaging/versioning Background in PySpark and distributed processing Familiarity with IDE-to-Databricks workflows such as Databricks Connect

Requirements

• 3+ years of experience in Python engineering with proficiency in packaging (wheels/pyproject), typing, clean architecture, error handling and performance mindset

• Proven background in production SDLC including Git workflows, automated tests, CI/CD, code reviews and release discipline

• Strong skills in Pandas and NumPy applied to production pipelines

• Familiarity with data formats such as Parquet and governed data access patterns

• Experience building and operating APIs and services using FastAPI, Flask or similar frameworks

• Competency in working within governed platform environments such as Databricks or containerized dev platforms

Zakres obowiązków

• Build and run production-grade Python applications including APIs and batch jobs with strong SDLC practices covering code reviews, testing, CI/CD, observability and documentation

• Develop robust batch and near-real-time data pipelines reading and writing governed storage with Parquet and columnar formats following approved patterns

• Transform quant and data science prototypes into deployable packages and services that are typed, modular and versioned

• Expose scoring and analytics capabilities via APIs or scheduled jobs rather than notebook-only deliverables

• Collaborate with platform teams on Databricks and Spark connectivity

• Optimize PySpark workloads when needed to ensure performance and scalability

• Maintain release discipline through Git workflows, automated tests and code reviews

• Operate within governed platform environments to ensure compliance and reliability

Seniority

• Senior

Mile widziane

• Skills in scikit-learn for production feature and scoring pipelines, including reproducible transforms and model packaging/versioning

• Background in PySpark and distributed processing

• Familiarity with IDE-to-Databricks workflows such as Databricks Connect