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Designs and oversees scalable data engineering architectures and implementations. The role translates business requirements into data solutions, guides engineering teams, ensures data quality and governance, optimizes performance, integrates systems, documents architecture, and supports project planning. It requires extensive Azure and Databricks expertise, proficiency in Python, PySpark, and SQL, knowledge of distributed computing and modern data platforms, and experience leading teams and working with stakeholders.

Build and productionize machine learning, generative AI, LLM, and automation solutions. Design industrialized processing pipelines, implement MLOps and LLMOps best practices, develop enterprise AI agents using Microsoft Copilot Studio and Azure AI Foundry, and integrate Microsoft and Azure services. Gather requirements, estimate work, present technical solutions, troubleshoot agents, and create documentation.

Design, build, and optimize GCP data ecosystems, including pipelines, data lakes, warehouses, storage architectures, and processing infrastructure. Gather requirements, model data, troubleshoot complex issues, tune performance, monitor systems, and support new functionality. Collaborate directly with clients and cross-functional teams, while mentoring junior data engineers and leading technical improvement discussions.

Lead data modeling for SAP domains including Supply Chain, Order-to-Cash, and Finance. Design and review conceptual, logical, and physical models; validate mappings, data structures, business rules, and data contracts; and ensure governance, quality, metadata, lineage, and canonical model alignment. Use Databricks and SQL to support integration, transformation, reporting, and analytics. Collaborate with data, engineering, SAP, architecture, and business stakeholders to resolve modeling gaps and approve production-ready designs.

Provides technical leadership for Azure data engineering projects, including solution architecture, data modeling, pipelines, data integration, code quality, QA, performance optimization, governance, security, and documentation. Collaborates with stakeholders and project managers to define scope, manage risks, and deliver scalable, efficient solutions. Requires strong Python, SQL, PySpark, Databricks, Azure Data Factory, Delta Lake, Synapse, Spark, data warehousing, governance, and Azure DevOps expertise.

Build and productionize machine learning, LLM, and AI solutions with Data Science teams. Design industrialized data-processing pipelines, implement MLOps and LLMOps frameworks, manage model lifecycles, and develop production Python, ML, and cloud solutions using Azure, GCP, Databricks, Spark, and PySpark. Gather requirements, estimate work, document solutions, present results, and support technical best practices.

The Data Scientist will implement GenAI systems, build components, support project delivery, analyze findings, and deliver technical solutions, focusing on NLP and deep learning.

Implement enterprise ML and GenAI solutions in production, including AI agents, LLM automation, RAG, knowledge grounding, and industrialized processing pipelines. Establish ML lifecycle, MLOps, and LLMOps best practices; integrate Microsoft AI and Power Platform services; support deployments across environments; gather requirements; estimate work; present solutions; and document technical implementations.

Implement machine learning models in production and design enterprise GenAI, LLM, and AI automation solutions. Build and manage industrialized processing pipelines, establish ML lifecycle and MLOps/LLMOps best practices, integrate Microsoft AI and Power Platform services, and support data science teams. The role also involves gathering requirements, estimating work, presenting technical solutions, troubleshooting AI agents, and creating documentation.

Develop end-to-end classification and forecasting models, including data preparation, exploratory analysis, feature engineering, model training, tuning, evaluation, visualization, and deployment support. Collaborate with data and AI engineers on productionization, monitoring, dashboards, and reproducible documentation. Apply Python, SQL, classical machine learning, time-series modeling, cloud platforms, and business-focused analytics to solve problems such as demand forecasting and churn prediction.