Snowflake Lead Data Engineering

Snowflake, Cortex AI, Python and Insurance Domain on AWS Cloud, Talend ETL Roles & Responsibilities The Snowflake Lead will serve as the onsite technical lead for CLEARBROOK ‘s enterprise data platform, responsible for end to end Snowflake solution design, development leadership, and AI/advanced analytics enablement. The role requires deep hands on expertise in Snowflake, strong data engineering fundamentals, and the ability to integrate AI/ML driven use cases into the Snowflake ecosystem while coordinating with offshore teams. 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

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