Manager of Data Engineering

eNGINE builds Technical Teams. We are a Solutions and Placement firm shaped by decades of interaction with Technical professionals. Our inspiration is continuous learning and engagement with the markets we serve, the talent we represent, and the teams we build. Our Consulting Workforce is encouraged to enjoy career fulfillment in the form of challenging projects, schedule flexibility, and paid training/certifications. Successful outcomes start and finish with eNGINE., The ideal candidate brings deep expertise across Azure, Microsoft Fabric, Databricks, Snowflake, modern software engineering, cloud-native architectures, and emerging AI technologies. They combine the technical depth to solve complex engineering challenges with the leadership ability to influence technology strategy across business and IT stakeholders.

• Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Science, or a related discipline \n

• 4+ years of hands-on data and AI engineering, including machine learning, advanced analytics, or intelligent automation \n

• Demonstrated success delivering enterprise-scale production AI solutions \n

• Exceptional communication and influence skills

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• Establish technical standards, solution architectures, and engineering patterns for all AI initiatives \n

• Lead technology decisions involving AI platforms, data platforms, cloud infrastructure, and intelligent automation \n

• Evaluate emerging AI technologies and determine their enterprise applicability \n

• Create scalable architectural patterns that balance innovation, maintainability, security, and cost \n

• Serve as the coach for AI engineering across the organization \n

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Enterprise AI Platform Ownership:

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• Design and evolve AI platform capabilities across Azure, Microsoft Fabric, Databricks, and Snowflake \n

• Establish platform strategies for Generative AI, RAG, machine learning, agent frameworks, vector search, and intelligent automation \n

• Define standards for AI lifecycle management, model deployment, monitoring, and observability \n

• Build reusable engineering capabilities that accelerate AI delivery \n

• Establish AI engineering guardrails, governance controls, and operational standards \n

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AI Application and Agent Development:

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• Lead the design and implementation of enterprise AI agents and copilots \n

• Architect Retrieval-Augmented Generation (RAG) solutions that leverage enterprise knowledge assets \n

• Design intelligent workflows that integrate AI into operational business processes \n

• Build scalable API and microservices architectures that expose AI capabilities across applications \n

• Drive adoption of AI-powered decision support, automation, and augmentation \n

• Establish standards for prompt engineering, orchestration, evaluation, and AI performance measurement \n

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Advanced Engineering Leadership:

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• Serve as principal architect for highly complex AI and data initiatives \n

• Lead technical design reviews and architecture governance \n

• Partner with engineering teams to solve difficult scalability, performance, reliability, and security challenges \n

• Mentor and manage two or more engineers \n

• Drive engineering excellence through best practices, standards, code quality, and technical coaching \n

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