Description

Lead the design, modernization, and scaling of cloud-native data architectures on AWS that power analytics and Agentic AI initiatives.

This role is hybrid.

Responsibilities

• Architect 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 including warehousing strategy, data sharing, streams & tasks, time travel, and zero-copy cloning.
• Design data foundations for 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.

Required Skills

• 8–10 years of experience in Big Data Engineering or Analytics.
• Expert-level Snowflake experience with 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.
• Proficient 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.

Preferred Skills

• Experience integrating LLMs, autonomous agents, or AI orchestration frameworks with data platforms.
• Familiarity with vector databases, embeddings, or AI-optimized data pipelines.
• Experience with dbt, Airflow, Kafka, Spark, or similar tools.

Key Skills

Education

Any Graduate