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Strong employer brand plus a common data-engineer title create moderate applicant competition.
Core data engineering skills transfer across industries, but GenAI and platform specifics raise domain sensitivity.
Explicit 6+ years requirement and many mandatory platform and GenAI skills increase filter rigidity.
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Lead AI-driven transformation by designing and implementing agentic AI, GenAI workflows, and intelligent automation across analytics and data platforms.
Develop, refactor, and maintain production-grade Python and PySpark data pipelines, APIs, and AI services with best practices for scalability and monitoring.
Manage and optimize Databricks and Snowflake analytics environments, including ETL pipelines on AWS Glue, with a focus on performance, cost, governance, and platform administration.
6+ years of experience in Data Engineering or Analytics Engineering roles.
Strong hands-on experience with GenAI, LLMs, agentic AI, and multi-agent orchestration.
Advanced Python development skills including frameworks like Flask or FastAPI, and strong PySpark expertise.
Expert proficiency with Databricks and Snowflake; solid understanding of ETL architectures using AWS Glue and cloud-native pipelines.
Technical leader capable of delivering end-to-end AI and data engineering solutions combining agentic AI and large-scale analytics.
Experienced in cloud data platform optimization, security governance, and AI integration with a focus on performance and cost control.
Comfortable working in complex regulated domains, applying DBA fundamentals and best practices to modern scalable cloud environments.