Senior Data Engineer

• Develop and implement accurate forecasting models to predict product demand using historical data.
• Leverage time series analysis techniques to generate business insights and drive data-driven decisions.
• Continuously optimize forecasting models to adapt to changing business needs and improve forecast accuracy.
• Apply advanced ML algorithms (e.g., XGBoost, Random Forest, Deep Learning) to solve complex business problems.
• Conduct exploratory data analysis (EDA) to uncover trends, anomalies, and actionable insights.
• Develop and validate machine learning models to support various analytical use cases.
• Design scalable and maintainable data pipelines and architectures to support analytical workflows.
• Ensure high data quality and consistency across multiple data sources.
• Collaborate with engineering teams to implement best practices in data storage, processing, and governance.
• Build and deploy data solutions on Databricks, leveraging its ML and Spark capabilities.
• Utilize AWS services (e.g., S3, EC2, Lambda, SageMaker) for scalable cloud-based model training and deployment.
• Ensure cost-effective and optimized use of cloud resources in project implementations.
• Design end-to-end analytics and ML solutions tailored to client needs.
• Lead technical discussions and contribute to solution architecture.
• Translate business requirements into technical specifications and deliver actionable results.
• Understand key metrics and pain points within manufacturing and supply chain operations.
• Use domain knowledge to frame problems accurately and drive impactful analytical outcomes.
• Collaborate with stakeholders to ensure alignment between analytics and business goals.
• Understand SAP modules related to supply chain and manufacturing (e.g., MM, PP, SD) Use SAP knowledge to enhance data modeling and business context understanding.

• Skills Required: o Strong proficiency in Python o Expertise in Time Series Forecasting and Demand Forecasting o Familiarity with Advanced ML Algorithms and Data Analysis o Must have experience in Data Architecture – practical exposure required o Hands-on experience with Databricks and AWS (should be visible in past projects) o Design and solutioning experience highly valued
• Strong background in data architecture, solution design, and end-to-end ML model lifecycle
• Experience as Data Architect, Solution Architect, or in equivalent architectural roles
• Should come from a product-based or data engineering-focused startup background

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