Work collaboratively with cross-functional teams to design, implement, and maintain data integration systems

Build and enhance data pipelines using Apache Spark, Microsoft Azure, and Python

Ensure data pipelines are reliable, scalable, and maintainable

Adapt data science models for production environments

Develop forecasting models to support strategic business decision-making

Maintain high levels of data quality and consistency across all sources

Monitor and optimize data pipelines to ensure efficient processing and performance

Collaborate with data scientists to design and implement machine learning models

Continuously refine data integration systems to meet changing business needs

Stay informed about emerging technologies and best practices in data software engineering

Minimum of 3 years of experience in data software engineering or related roles

Advanced knowledge of Apache Spark, Microsoft Azure, and Python

Strong understanding of forecasting models, data science workflows, and MLOps principles

Hands-on experience with Databricks for building and optimizing data pipelines

Proven ability to operationalize data science models for production use

Proficiency in Git for version control and team collaboration

Solid grasp of Azure concepts, including clouds, regions, ADLS, and compute services

Exceptional analytical and problem-solving skills with a creative approach to technical challenges

Familiarity with Agile methodologies and development environments

Advanced English communication skills, both written and spoken, at a B2+ level

Experience with Panda for data manipulation and analysis

Strong knowledge of SQL and relational database systems

Understanding of statistical models and experience developing them with Python or Spark

Familiarity with machine learning tools and related technologies