Collaborate with cross-functional teams to design, develop, and maintain data integration solutions

Create and optimize data pipelines using Apache Spark, Microsoft Azure, and Python

Ensure data pipelines are robust, scalable, and easy to maintain

Transform data science models into production-ready systems

Build and manage forecasting models to support critical business decisions

Ensure consistent data quality and integrity across all sources

Monitor and refine data pipelines to improve performance and efficiency

Partner with data scientists to design and deploy machine learning models

Continuously enhance data integration solutions to meet evolving business needs

Stay updated on the latest technologies and trends in data software engineering

At least 3 years of experience in data software engineering or a similar role

Expertise in Apache Spark, Microsoft Azure, and Python

Strong knowledge of forecasting models, MLOps practices, and data science principles

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

Proven ability to operationalize data science models for production environments

Proficiency with Git for version control and collaboration

Solid understanding of Azure concepts such as clouds, regions, ADLS, and compute services

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

Experience working in Agile development environments

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

Experience with Panda for data manipulation and analysis

Advanced knowledge of SQL and relational database systems

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

Familiarity with tools and technologies related to machine learning