Snowflake Data Engineer

Job Description

Job Title: Snowflake Data Engineer

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Location: Washington DC (Hybrid – 2-3 days in office)

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Contract

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Job Description:

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Build and manage the modern cloud data platform across ingestion, transformation, and delivery layers.

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Role Summary

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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.

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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.

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Key Responsibilities

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Design, configure, and maintain the Snowflake platform, including databases, schemas, warehouses, roles, security policies, and environment setup across development, test, and production.

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Implement and manage cloud-based ingestion using Fivetran for legacy, operational, and SaaS source systems, including full-load, incremental, and CDC-based patterns.

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Build and optimize the target data architecture across raw, staging, curated, and reporting-ready layers, aligned to medallion-style or equivalent modular design patterns.

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Develop, test, and maintain dbt models that replace legacy SSIS transformation logic and support curated business-ready data assets for reporting, reconciliation, and analytics.

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Translate legacy constructs such as SCD Type 2 handling, lookup logic, conditional branching, and other ETL patterns into modern Snowflake and dbt implementations.

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Configure AWS integrations required for the data platform, including S3 stages, IAM roles, storage integrations, encryption support, and secure connectivity patterns.

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Establish and support source-to-target mappings, metadata consistency, schema evolution handling, and documentation of field-level lineage across ingestion and transformation layers.

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Implement automated data quality checks, freshness checks, reconciliation controls, and exception handling to improve trust in the data before it reaches reporting layers.

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Monitor ingestion pipelines, connector health, transformation runs, and Snowflake workloads; troubleshoot failures, schema drift, performance issues, and data incidents.

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Optimize Snowflake cost and performance through workload isolation, warehouse sizing, clustering, query tuning, and platform monitoring.

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Support downstream analytics and reporting teams by delivering trusted, well-documented, analytics-ready data structures compatible with Sigma and other governed reporting tools.

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Contribute to CI/CD, release automation, and Git-based engineering workflows for dbt, Snowflake, and data pipeline changes.

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Produce operational documentation, configuration standards, runbooks, and handover materials for ongoing support and client operations teams.

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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.

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Required Skills and Experience

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Strong hands-on experience implementing Snowflake in enterprise environments.

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Deep knowledge of SQL, query optimization, performance tuning, and warehouse design.

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Experience migrating from legacy EDW platforms such as SQL Server, SSIS, and SSAS.

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Exp. with dbt, Fivetran, AWS, and Sigma BI/reporting integrations.

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Hands-on experience with Fivetran or comparable cloud ingestion tools.

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Strong AWS fundamentals: S3, IAM, KMS, VPC, PrivateLink for Snowflake connectivity

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Strong understanding of source-to-target mapping, CDC concepts, and incremental loading.

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Experience working with Snowflake and cloud-based data platforms.

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Ability to write SQL for data validation and reconciliation between source and Bronze landing tables

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NICE TO HAVE

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Snowflake SnowPro Core or Advanced: Data Engineer certification

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Experience with Snowflake cost optimization on large-scale financial data workloads

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Familiarity with dbt project structure and Snowflake-specific dbt materializations (dynamic tables