Note: The job is a remote job and is open to candidates in USA. VOLTO Consulting is seeking a Snowflake Lead to serve as the onsite technical lead for CLEARBROOK's enterprise data platform. The role leads Snowflake architecture, development, production support, data integration, security, and AI/ML enablement for insurance data on AWS while coordinating with offshore teams.
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
- Perform root cause analysis for job failures and data analysis & fixes
- Perform Month End Closing Activities
- 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
- 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
- Enable AI/ML Use Cases On Snowflake, Including
- Feature engineering datasets for ML models
- Snowpark (Python) based data processing
- Integration with external ML platforms (Databricks / SageMaker / Azure ML where applicable)
- Support AI Driven Insights Such As
- Claims triage & risk scoring
- Fraud detection inputs
- Premium leakage and pricing analytics
- Guide teams in using Python, SQL, and Snowflake native capabilities for data science workloads
Skills
- 12 - 15 years of experience
- Snowflake, Cortex AI, Python and Insurance Domain on AWS Cloud, Talend ETL
- Deep hands on expertise in Snowflake
- Strong data engineering fundamentals
- Ability to integrate AI/ML driven use cases into the Snowflake ecosystem
- Snowflake Production Support
- Perform root cause analysis for job failures and data analysis & fixes
- Perform Month End Closing Activities
- 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
- 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
- Enable AI/ML Use Cases On Snowflake
- Feature engineering datasets for ML models
- Snowpark (Python) based data processing
- Integration with external ML platforms (Databricks / SageMaker / Azure ML where applicable)
- Support AI Driven Insights Such As Claims triage & risk scoring, Fraud detection inputs, 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)
- 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)
- Strong communication skills for onsite customer interaction
- Ability to translate business requirements into scalable data & AI solutions
- Python: Snowpark (preferred)
- Strong understanding of cloud data platform architecture (AWS preferred)
- Insurance domain experience (P&C / Specialty Insurance strongly preferred)
Benefits
- Remote
- Fulltime
Company Overview