Snowflake Lead – Data Engineering (with Cortex AI exp)

Job Description:
Must Have Technical/Functional Skills
Snowflake, Cortex AI, Python and Insurance Domain on AWS Cloud, Talend ETL

Roles & Responsibilities

Snowflake Production Support
• Perform root cause analysis for job failures and data analysis & fixes
• Perform Month End Closing Activities
Snowflake Development & Architecture
• Lead design and development of Snowflake schemas, tables, views, streams, tasks, and Snowpipes
• Define and enforce best practices for performance optimization (warehouse sizing, clustering, query tuning)
• Own Snowflake security architecture: RBAC, role hierarchy, data masking, row/column level security
• Oversee promotion of code across environments using CI/CD practices
Data Engineering & Integration
• Lead development of batch and near real time ingestion pipelines using Talend / Qlik Replicate / Snowpipe
• Ensure data quality checks, reconciliation, and schema drift handling
• Guide integration from insurance source systems (Policy, Claims, Billing, Reinsurance) into Snowflake
• Provide technical oversight for SQL, Python, and ELT based transformations
AI / Advanced Analytics Enablement
• Enable AI/ML use cases on Snowflake, including:
o Feature engineering datasets for ML models
o Snowpark (Python)–based data processing
o Integration with external ML platforms (Databricks / SageMaker / Azure ML where applicable)
• Support AI driven insights such as:
o Claims triage & risk scoring
o Fraud detection inputs
o Premium leakage and pricing analytics
• Guide teams in using Python, SQL, and Snowflake native capabilities for data science workloads

Core Technical Skills
• Snowflake: Advanced SQL, Performance Tuning, Security, Snowpipe, Streams & Tasks
• Data Engineering: ELT/ETL patterns, data modeling, CDC concepts
• Python: Data processing, automation, Snowpark (preferred)
• Strong understanding of cloud data platform architecture (AWS preferred)
AI / Analytics Skills
• Hands on

exposure to AI/ML pipelines (feature preparation, training data creation)
• Experience supporting ML models through data engineering and operationalization
• Familiarity with Python ML libraries (scikit learn, pandas, NumPy) – applied from a data engineering perspective
• Understanding of model lifecycle support (data refresh, monitoring, retraining inputs)
Domain & Soft Skills
• Insurance domain experience (P&C / Specialty Insurance strongly preferred)
• Strong communication skills for onsite customer interaction
• Ability to translate business requirements into scalable data & AI solutions