Lead Data Engineer / Snowflake Engineer (AWS, Agentic AI)

Role Overview

We are hiring a Lead Data Engineer / Snowflake Engineer with deep expertise in Snowflake on AWS and hands-on exposure to Agentic AI / Generative AI–driven data platforms. This role will lead the design, modernization, and scaling of cloud‑native data architectures that power analytics and AI initiatives.

Key Responsibilities

• Architect, design, and optimize Snowflake data platforms on AWS for high performance and cost efficiency
• Lead end‑to‑end ELT/ETL pipelines using Snowflake, AWS services, and modern data engineering tools
• Implement advanced Snowflake features (Performance Optimization, Warehousing strategy, Data Sharing, Streams & Tasks, Time Travel, Zero Copy Cloning)
• Design data foundations that support Agentic AI and GenAI workloads, including AI‑ready datasets, vectorized data, and metadata‑driven pipelines
• Collaborate with AI/ML teams to enable autonomous agents, LLM‑driven analytics, and intelligent data orchestration
• Provide technical leadership, code reviews, and mentoring to data engineering teams
• Partner with business and product stakeholders to translate analytics and AI requirements into scalable data solutions

Required Skills & Experience

• 8–10 years of experience in Big Data Engineering / Analytics
• Expert‑level Snowflake experience, including large‑scale production deployments
• Strong hands-on experience with AWS (S3, EC2, Lambda, Glue, Redshift/Athena, IAM, CloudWatch, Step Functions)
• Proven experience building cloud‑native data architectures on AWS
• Solid programming skills in Python and SQL
• Experience with data modeling for analytics and AI use cases
• Hands-on or applied exposure to Agentic AI, Generative AI, or AI‑driven data platforms
• Experience leading or mentoring engineering teams in enterprise environments

Highly Desirable

• Experience integrating LLMs, autonomous agents, or AI orchestration frameworks with data platforms
• Exposure to vector databases, embeddings, or AI‑optimized data pipelines
• Experience with dbt, Airflow, Kafka, Spark, or similar tools
• Prior onsite experience in large, complex enterprise data ecosystems