We are seeking a highly skilled and motivated Data Engineer. The ideal candidate will have strong expertise in Databricks, PySpark, and Time Series Forecasting, with experience in managing and scaling data pipelines for analytics and machine learning use cases. A background in the retail domain and familiarity with MLflow and optimization techniques will be considered a strong advantage.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using Databricks and PySpark
• Implement robust data workflows to support Time Series Forecasting and analytics use cases
• Collaborate with data scientists and analysts to productionize machine learning models
• Leverage MLflow for model tracking, experiment management, and deployment (if applicable)
• Optimize data processing jobs for performance and cost-efficiency
• Ensure data quality, governance, and security across all pipelines
• Work closely with cross-functional teams, including product, business, and engineering
• Contribute to architecture and data modeling discussions, especially in the context of retail datasets

Required Skills

• Strong hands-on experience with Databricks (Must)
• Proficiency in PySpark for distributed data processing (Must)
• Deep understanding of Time Series Forecasting techniques and implementations (Must)
• Experience with MLflow or similar MLOps platforms (Good to have)
• Familiarity with mathematical optimization and operations research techniques (Good to have)
• Experience working in or understanding of the Retail domain (Good to have)
• Solid understanding of data warehousing, ETL pipelines, and cloud data ecosystems (Azure/AWS/GCP)
• Strong problem-solving and analytical skills
• Excellent communication and collaboration abilities