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Tier-1 employer, mid-level generalist title, and common data/AI skillset increase applicant density.
Core cloud data engineering and AI skills are broadly transferable across industries.
Explicit 5-8 years plus mandatory Databricks, AWS, and LLM/Vector expertise increases shortlisting strictness.
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Design, build, and maintain scalable data pipelines, data lakehouse solutions, and analytics-ready data products using Databricks, AWS, and cloud-native services.
Implement and optimize AI-powered applications including Generative AI, LLMs, Retrieval-Augmented Generation, vector embeddings, and AI Agents, leveraging modern AI frameworks and tools.
Drive operational excellence via data quality, governance, CI/CD pipelines, automation, and mentor teams to enforce engineering best practices in a product-oriented agile environment.
5-8 years of hands-on experience in Data Engineering, Software Engineering, or Cloud Data Platforms.
Expert-level skills with Databricks, PySpark, Python, SQL, AWS data services (Glue, Lambda, S3, Athena, Redshift), and distributed data processing frameworks.
Experience with Generative AI, Large Language Models, RAG, Vector databases, and AI Agent frameworks is mandatory.
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Information Systems, or related field.
Deep expertise in cloud-based Lakehouse architectures and enterprise-scale data platform engineering on AWS and Databricks.
Proven ability to lead complex technical initiatives, mentor teams, and collaborate cross-functionally in agile, product-centric settings.
Strong practical experience leveraging AI-assisted development tools and AI orchestration frameworks to innovate and improve engineering productivity.