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

Requirements

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SnowflakeAWSPythonSQLELTETLdata modelingdata architectureAI-ready datasetsvectorized data

Soft Skills

technical leadershipmentoringcollaborationcommunication