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Experience: 16–20 years; ideal profile has 15+ years in Data & Analytics.
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Location: Chicago, IL — Hybrid, 3 days/week in office.
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Core requirement: Strong Azure + Databricks + Data Engineering + Agentic AI experience.
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Data Platform: Azure Databricks, Delta Lake, Unity Catalog, ADLS Gen2, ADF, Databricks Workflows/DLT.
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Data Engineering: PySpark, Spark SQL, Python, SQL, ETL/ELT modernization.
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Agentic AI: AI Agents for ingestion, mapping, schema evolution, data quality, pipeline optimization, RCA, governance, and self-healing.
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GenAI: Azure OpenAI, RAG, LangChain, LangGraph, Semantic Kernel, AutoGen, MCP, Vector DBs.
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AI-DLC: AI-assisted requirements, data modeling, code/pipeline generation, testing, code review, documentation, deployment, and monitoring.
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Governance: Responsible AI, guardrails, security, auditability, observability, cost monitoring, explainability, and HITL.
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DevOps/MLOps: Azure DevOps, GitHub Actions, CI/CD, MLOps, LLMOps, AI evaluation/testing.
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Leadership: Enterprise architecture, reusable AI agents/accelerators, mentoring teams, and CXO-level AI strategy/ROI discussions.
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Certifications: Databricks, Azure Solution Architect/Data Engineer, Azure OpenAI, GenAI/Agentic AI certifications are preferred.
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Education: BE/ME/BTech/MTech/BSc/MSc or equivalent engineering/science degree.
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Key success expectation: Drive 30–50% improvement in Data Engineering productivity through Agentic AI.
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