Lead Data & AI Engineer

We are seeking a Lead Data & AI Engineer to design, build, and advance enterprise data and AI capabilities across a global organization. This role will play a key part in developing a modern, cloud-based data platform and implementing practical AI solutions that improve business intelligence, operational efficiency, and decision-making across the organization.

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The ideal candidate is a hands-on technical leader who can move between data architecture, engineering, analytics, and applied AI, translating business needs into scalable technical solutions.

• 8+ years of experience in data engineering, data architecture, analytics, AI/ML engineering, or a related discipline. \n

• Strong hands-on experience designing and building enterprise data platforms, data lakes, data warehouses, and scalable data pipelines. \n

• Experience with one or more major cloud platforms: Azure, AWS, or GCP. \n

• Strong SQL and Python development skills. \n

• Practical experience developing and deploying AI/ML solutions, including generative AI, LLMs, RAG, predictive modeling, and AI-powered automation. \n

• Experience with modern data and analytics technologies such as Snowflake, Databricks, Microsoft Fabric, BigQuery, Power BI, Tableau, or similar platforms. \n

• Experience integrating data from enterprise applications such as SAP, Microsoft Dynamics, Salesforce, and other ERP/CRM systems. \n

• Understanding of data governance, data quality, master data management, and enterprise data security. \n

• Ability to work directly with business stakeholders, understand operational challenges, and translate requirements into technical solutions. \n

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• Data Engineering & Architecture: Design and build a modern, cloud-native enterprise data lake and data platform, integrating data from ERP, CRM, supply chain, financial, and other business systems. \n

• Data Pipelines & Integration: Develop scalable batch and real-time data pipelines, ETL/ELT processes, data models, and integrations across multiple enterprise data sources. \n

• AI & Machine Learning: Develop and implement AI/ML solutions, including predictive analytics, forecasting, anomaly detection, intelligent automation, and generative AI applications. \n

• Generative AI & AI Agents: Build and deploy LLM-powered applications, RAG solutions, conversational AI assistants, and intelligent agents that connect to enterprise data and automate business workflows. \n

• Business Intelligence & Analytics: Develop enterprise reporting, dashboards, semantic data models, and self-service analytics capabilities using tools such as Power BI, Tableau, or similar platforms. \n

• Data Governance & Quality: Establish data quality standards, metadata management, data lineage, master data practices, and appropriate security and access controls. \n

• Business Collaboration: Work directly with stakeholders across Finance, Sales, Marketing, Operations, Supply Chain, and other functions to identify opportunities and deliver data and AI solutions with measurable business value. \n

• Technical Leadership: Establish engineering standards and best practices, evaluate emerging technologies, and provide technical guidance across data engineering, analytics, and AI initiatives. \n

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