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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Integration & Agentic Analytics Engineering Lead - **Company:** Insight Global - **Location:** Alpharetta, GA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Application Integration Architecture, Computing Platforms, Microsoft Azure, Cloud Computing, Information Engineering, Data Governance, Extract Transform Load (ETL), DevOps, Dimensional Modeling, Distributed Systems, Graph Database, Python (Programming Language), OpenAI, DataOps, SAP (Applications), Software Engineering, SQL Databases, Systems Integration, Enterprise Data Management, Workday Financials, Oracle Hyperion, Data Ingestion, Azure Data Factory, LangChain, Retrieval-Augmented Generation, Large Language Models, Snowflake, Apache Spark, Generative AI, Agentic-AI, Data Layers, Event Driven Architecture, Microsoft Fabric, Pyspark, Kubernetes, Data Analytics, Apache Kafka, Data Management, Model Context Protocol, Terraform, Semantic Kernel, Data Pipelines, Docker, Databricks - **Published:** October 11, 2026 - **Apply:** https://www.techcareers.com/job.asp?id=3435057419&tx=FK4946FFL&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role A successful candidate will be a technical leader who combines strong Data Engineering expertise with hands-on AI implementation experience. They should be actively deploying AI solutions in enterprise environments today, understand governance and responsible AI practices, and have a track record of leading engineering teams while remaining close to architecture and technical decision-making. The ideal profile is someone who can bridge Data Engineering, Enterprise Analytics, and Generative AI to drive business transformation across the organization. Skills and Requirements 7+ years of experience in Data Engineering, Analytics Engineering, Data Platforms, or Enterprise Data Solutions. 2-3+ years of recent people leadership experience managing technical teams. Strong hands-on experience with Databricks and Azure Data Platform technologies. Expertise with Python, SQL, Spark, and/or PySpark. Experience building and supporting enterprise-scale data platforms and Lakehouse architectures. Demonstrated experience deploying AI/GenAI solutions in production environments. Experience with AI Agents, Agentic Workflows, RAG, LLMs, or similar modern AI technologies. Strong understanding of enterprise data integrations, APIs, and distributed systems. Experience delivering executive reporting, analytics, and self-service BI solutions. Experience with dimensional modeling, semantic layers, and enterprise data modeling. Strong stakeholder management and communication skills with both technical and business audiences. Proven ability to lead multiple initiatives while coaching and developing technical teams. Experience with LangChain, LangGraph, Semantic Kernel, MCP (Model Context Protocol), or other agentic AI frameworks. Experience with Azure OpenAI or enterprise GenAI platforms. Familiarity with vector databases, knowledge graphs, and advanced RAG architectures. Experience with Microsoft Fabric. Experience with Snowflake and/or dbt. Experience with financial reporting systems and ERP platforms such as SAP, Oracle Financials, or Workday Financials. Experience with Master Data Management (MDM), Data Governance, Data Quality, or Data Observability initiatives. Experience with event-driven architectures using Kafka or Azure Event Hub. Experience with Terraform, Kubernetes, Docker, and modern DevOps practices. Previous experience supporting Finance, Enterprise Reporting, or Corporate Analytics organizations. ## Description The Manager, AI Integration & Agentic Analytics Engineering will lead a team of 5-7 analytics engineers, data engineers, and AI developers responsible for building enterprise data products, modern reporting solutions, and AI-powered automation capabilities. This individual will spend approximately 80% of their time driving strategic initiatives, proof-of-concepts, architecture decisions, and enterprise AI adoption while dedicating 20% to people leadership, coaching, and team management. On a daily basis, this person will: Lead development of enterprise data products and cloud-based analytics platforms using Databricks and Azure technologies. Drive the implementation of AI-powered solutions including AI Agents, Agentic Workflows, RAG architectures, and LLM-enabled analytics capabilities. Partner with Finance, Product, Operations, and Technology stakeholders to define and deliver reporting, automation, and data platform initiatives. Oversee the design and development of scalable ETL/ELT pipelines, integrations, and data ingestion frameworks. Lead architecture discussions around semantic models, financial reporting solutions, and enterprise analytics. Evaluate and implement emerging AI technologies that improve engineering productivity and business decision-making. Mentor engineers on modern software engineering practices, AI-assisted development, cloud technologies, and data engineering best practices. Drive proof-of-concepts and innovation initiatives focused on intelligent automation and enterprise AI adoption. Collaborate with architecture, security, and governance teams to ensure compliant and responsible deployment of AI solutions. Present technical strategies and initiative progress to senior leadership and business stakeholders.