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Tier-1 brand, metro location, and mid-level generalist Data/AI title drive high competition.
Core data engineering, Databricks and cloud skills are highly transferable across industries, so low sensitivity.
Explicit 5-8 year requirement plus many mandatory Databricks, AWS and LLM skills implies high strictness.
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Design, develop, and maintain scalable batch, streaming, and real-time data pipelines and analytics-ready data products using Databricks, AWS, and modern Lakehouse architectures.
Implement and optimize AI-powered solutions including Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and agentic AI with frameworks like LangChain or AutoGen.
Lead cloud-native data engineering best practices including CI/CD, Infrastructure as Code (IaC), data governance, security, and mentoring engineering teams.
5-8 years of hands-on experience in Data Engineering, Software Engineering, or Cloud Data Platforms.
Expert-level proficiency in Databricks, Python, PySpark, SQL, and AWS data & analytics services (Glue, Lambda, S3, Athena, Redshift, Lake Formation).
Hands-on experience with Generative AI, LLMs, RAG, Vector Databases, and AI Agent development frameworks.
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field.
Proven ability to lead complex technical initiatives and mentor engineering teams in an agile, product-oriented environment with end-to-end ownership.
Deep expertise in building enterprise-scale data pipelines and AI-powered data products leveraging Databricks Lakehouse and AWS ecosystems.
Experienced with AI-assisted engineering tools and agentic AI orchestration frameworks, indicating strong alignment with cutting-edge AI/ML solution development.