Snowflake Data Engineer

• Job Title: Snowflake Data Engineer
• Location: Washington DC (Hybrid – 2-3 days in office)

Job Description:

• Purpose
• Build and manage the modern cloud data platform across ingestion, transformation, and delivery layers.
• Role Summary
• This role combines Snowflake engineering, Fivetran-based ingestion, and dbt analytics engineering into one end-to-end data engineering position. The Data Engineer will modernize legacy SQL Server / SSIS / SSAS workloads into a scalable cloud platform with stronger automation, validation, lineage, and reporting readiness.
• This role leads the technical implementation of data storage, transformation, access patterns, and performance optimization in Snowflake, while ensuring the platform supports business reporting, historical analysis, governance, and future AI-enabled use cases.

• Key Responsibilities
• Design, configure, and maintain the Snowflake platform, including databases, schemas, warehouses, roles, security policies, and environment setup across development, test, and production.
• Implement and manage cloud-based ingestion using Fivetran for legacy, operational, and SaaS source systems, including full-load, incremental, and CDC-based patterns.
• Build and optimize the target data architecture across raw, staging, curated, and reporting-ready layers, aligned to medallion-style or equivalent modular design patterns.
• Develop, test, and maintain dbt models that replace legacy SSIS transformation logic and support curated business-ready data assets for reporting, reconciliation, and analytics.
• Translate legacy 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 support source-to-target mappings, metadata consistency, schema evolution handling, and documentation of field-level lineage across ingestion and transformation layers.
• Implement automated data quality checks, freshness checks, reconciliation controls, and exception handling to improve trust in the data before it reaches reporting layers.
• Monitor ingestion pipelines, connector health, transformation runs, and Snowflake workloads; troubleshoot 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 downstream 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.
• Work closely with architects, analysts, reporting teams, and client stakeholders to ensure the solution improves automation, reduces manual dependency, and supports a more scalable operating model.

• Required Skills and Experience
• Strong hands-on experience implementing Snowflake in enterprise environments.
• Deep knowledge of SQL, query optimization, performance tuning, and warehouse design.
• Experience migrating from legacy EDW platforms such as SQL Server, SSIS, and SSAS.
• Exp. with dbt, Fivetran, AWS, and Sigma BI/reporting integrations.
• Hands-on experience with Fivetran or comparable cloud ingestion tools.
• Strong AWS fundamentals: S3, IAM, KMS, VPC, PrivateLink for Snowflake connectivity
• Strong understanding of source-to-target mapping, CDC concepts, and incremental loading.
• Experience working with Snowflake and cloud-based data platforms.
• Ability to write SQL for data validation and reconciliation between source and Bronze landing tables
• NICE TO HAVE
• Snowflake SnowPro Core or Advanced: Data Engineer certification
• Experience with Snowflake cost optimization on large-scale financial data workloads
• Familiarity with dbt project structure and Snowflake-specific dbt materializations (dynamic tables)