Description

You will architect and optimize Snowflake data platforms on AWS, ensuring high performance and cost efficiency. You will own end-to-end ELT/ETL pipelines and implement advanced Snowflake features such as Data Sharing, Streams & Tasks, and Zero Copy Cloning. Your work will establish data foundations for Agentic AI and GenAI workloads, including vectorized data and metadata-driven pipelines. You will collaborate with AI/ML teams to enable autonomous agents and LLM-driven analytics while providing technical leadership, code reviews, and mentoring to the data engineering team.

Responsibilities

• Architect and optimize Snowflake data platforms on AWS for scale and cost efficiency.
• Lead end-to-end ELT/ETL pipeline development using Snowflake and AWS services.
• Implement advanced Snowflake capabilities including performance tuning, warehousing strategy, and time travel.
• Design AI-ready data foundations, including vectorized datasets and metadata-driven architectures.
• Mentor engineering teams and partner with stakeholders to translate analytics requirements into scalable solutions.

Required Skills

• 10+ years of experience in Big Data Engineering or Analytics.
• Expert-level proficiency with Snowflake, including large-scale production deployments.
• Strong hands-on experience with AWS services: 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.
• Applied exposure to Agentic AI, Generative AI, or AI-driven data platforms.
• Experience leading or mentoring engineering teams in enterprise environments.

Preferred Skills

• Bachelor’s degree in Computer Science, Engineering, or a related field.

Key Skills

Education

Bachelor’s degree