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

Build and optimize data pipelines leveraging Apache Spark, Microsoft Azure, and Python

Ensure data pipelines are scalable, reliable, and maintainable

Convert data science models into production-ready applications

Develop and support forecasting models to inform strategic business decisions

Maintain data quality and consistency across all data sources

Monitor and enhance data pipelines to ensure efficient data processing

Work with data scientists to create and deploy machine learning models

Continuously refine and improve data integration solutions to meet changing business requirements

Stay informed about emerging trends and technologies in data software engineering

A minimum of 3 years of experience in data software engineering or a related role

Advanced proficiency in Apache Spark, Microsoft Azure, and Python

Deep understanding of forecasting models, MLOps practices, and data science concepts

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

Proven ability to deploy data science models in production environments

Proficiency in using Git for version control

Strong knowledge of Azure concepts, including clouds, regions, ADLS, and compute services

Exceptional analytical and problem-solving skills, with the ability to think critically and innovate solutions

Experience working in Agile development environments

Strong written and verbal English communication skills at a B2+ level

Experience with Panda for data manipulation and analysis

In-depth knowledge of SQL and relational database systems

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

Familiarity with machine learning tools and technologies