Requires specialized depth and/or breadth of expertise. Interprets internal or external business issues and recommends best practices. Solves complex problems; takes a broad perspective to identify innovative solutions. Works independently, with guidance in only the most complex situations. May lead functional teams or projects.

Key Responsibilities:

• Design, build, and maintain scalable, reliable data pipelines to support AI/ML models and analytics in global supply chain and planning operations.
• Collaborate with data scientists and AI engineers to prepare and transform structured and unstructured data for modeling and simulation.
• Develop and optimize ETL/ELT processes using tools like Databricks, Spark.
• Ensure data quality, versioning, and lineage across the ML lifecycle.
• Support the deployment and monitoring of AI models in production environments.
• Translate business requirements into technical solutions, working closely with cross-functional teams.
• Contribute to the development of data services and APIs that power intelligent decision-making tools.

Requirements :

• 12-14 Years of strong experience in Data Architect roles
• Strong expertise in SQL, Python, and distributed data processing (e.g., Spark, PySpark).
• Experience with cloud-based data platforms (Azure).
• Familiarity with MLOps tools and practices (e.g., MLflow) is a strong advantage.
• Experience working with AI/ML frameworks (e.g., TensorFlow, PyTorch) or supporting model training and deployment is a strong advantage.
• Understands the role of data in driving supply chain and planning decisions.
• Able to align data engineering solutions with business KPIs and operational goals.
• May lead technical workstreams or mentor other engineers.
• Takes ownership of data infrastructure components and contributes to architectural planning.
• Solves complex data challenges with creativity and precision.
• Proactively identifies and resolves data quality, performance, and scalability issues.
• Enables the success of AI/ML initiatives by ensuring robust, scalable, and high-quality data infrastructure.
• Supports real-time and predictive decision-making across global operations.
• Communicates effectively with data scientists, AI engineers, and business stakeholders.
• Comfortable working in a fast-paced, collaborative environment with evolving priorities.