Work closely with cross-functional teams to design, build, and maintain data integration solutions

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

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

Transform data science models into production-ready solutions

Create and maintain forecasting models to support business decision-making processes

Ensure data consistency and quality across all data sources

Monitor and fine-tune data pipelines for efficient and effective data processing

Collaborate with data scientists to develop and implement machine learning models

Continuously enhance and improve the data integration solution to meet evolving business needs

Stay updated on emerging technologies and best practices in data software engineering

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

Advanced expertise in Apache Spark, Microsoft Azure, and Python

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

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

Proven ability to operationalize data science models for production environments

Proficiency with Git for version control

Solid understanding of Azure concepts, including clouds, regions, ADLS, and compute

Strong analytical and problem-solving skills, with the ability to think critically and creatively

Experience working in Agile development teams and environments

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

Experience using Panda for data manipulation and analysis

Strong knowledge of SQL and relational database structures

Understanding of statistical models and experience developing models using Python or Spark

Familiarity with tools and technologies in the machine learning space