Job Summary:

We are seeking a Snowflake Data Engineer with strong expertise in Snowflake, Fivetran, dbt, ETL, and AWS cloud services to support the modernization of legacy SQL Server, SSIS, and SSAS workloads into a scalable cloud-based data platform. The role combines Snowflake engineering, cloud-based data ingestion, and analytics engineering to deliver reliable data storage, transformation, access patterns, automation, validation, lineage, and reporting-ready data. The ideal candidate will have strong SQL and data engineering skills and experience supporting governance, historical analysis, performance optimization, and future AI-enabled use cases.

Key Responsibilities
• Design, configure, and maintain Snowflake databases, schemas, warehouses, roles, security policies, and environments across development, test, and production.
• Implement and manage cloud-based data ingestion using Fivetran for legacy, operational, and SaaS source systems, including full-load, incremental, and CDC-based patterns.
• Build and optimize data architecture across raw, staging, curated, and reporting-ready layers using medallion-style or equivalent modular design patterns.
• Develop, test, and maintain dbt models to replace legacy SSIS transformation logic and deliver curated, business-ready data assets for reporting, reconciliation, and analytics.
• Translate legacy data-processing constructs such as SCD Type 2 handling, lookup logic, conditional branching, and other ETL patterns into modern Snowflake and dbt implementations.
• Configure AWS integrations required for the data platform, including S3 stages, IAM roles, storage integrations, encryption support, and secure connectivity patterns.
• Establish and maintain source-to-target mappings, metadata consistency, schema evolution handling, and field-level lineage documentation across ingestion and transformation layers.
• Implement automated data quality checks, freshness checks, reconciliation controls, and exception handling to improve data reliability before it reaches reporting layers.
• Monitor ingestion pipelines, connector health, transformation runs, and Snowflake workloads.
• Troubleshoot pipeline failures, schema drift, performance issues, and data incidents.
• Optimize Snowflake cost and performance through workload isolation, warehouse sizing, clustering, query tuning, and platform monitoring.
• Support analytics and reporting teams by delivering trusted, well-documented, analytics-ready data structures compatible with Sigma and other governed reporting tools.
• Contribute to CI/CD, release automation, and Git-based engineering workflows for dbt, Snowflake, and data pipeline changes.
• Produce operational documentation, configuration standards, runbooks, and handover materials for ongoing support and client operations teams.
• Collaborate with architects, analysts, reporting teams, and stakeholders to improve automation, reduce manual dependencies, and support a scalable operating model.

Required Qualifications
• Strong hands-on experience implementing and supporting Snowflake in enterprise environments.
• Strong experience with Snowflake, Fivetran, dbt, ETL, AWS cloud, and AWS services.
• Deep knowledge of SQL, query optimization, performance tuning, and data warehouse design.
• Experience migrating or modernizing legacy EDW workloads involving SQL Server, SSIS, and SSAS.
• Hands-on experience with Fivetran or comparable cloud-based data ingestion tools.
• Experience with dbt and analytics engineering practices.
• Strong AWS fundamentals, including S3, IAM, KMS, VPC, and PrivateLink for Snowflake connectivity.
• Strong understanding of source-to-target mapping, CDC concepts, incremental loading, and cloud-based data platforms.
• Ability to write SQL for data validation and reconciliation between source systems and Bronze landing tables.
• Experience with Sigma BI/reporting integrations.
• Strong understanding of data quality, reconciliation, metadata, lineage, and schema evolution.

Preferred Qualifications
• Snowflake SnowPro Core or Advanced: Data Engineer certification.
• Experience with Snowflake cost optimization for large-scale financial data workloads.
• Familiarity with dbt project structure and Snowflake-specific dbt materializations, including dynamic tables.