Snowflake Data Engineer

Fortune 500; San Antonio (TX); On-site; USD 120,000 - 180,000; Part time

Job summary

Fortune 500 is seeking an experienced Snowflake Data Engineer to ingest, model, and transform core financial data into Snowflake.

The role emphasizes Snowflake, SQL, dbt, dimensional modeling, and banking data. You will design models, build pipelines, ensure data quality, and collaborate across Linux and Azure environments with a focus on security and scalable analytics.

Qualifications

• Hands-on with core banking or financial data platforms.
• Strong Snowflake architecture and advanced SQL.
• Experience with semi-structured data (JSON, VARIANT).
• Proven dbt production models, tests and docs.
• Dimensional modeling with Kimball and SCDs.
• Banking/financial data experience preferred.
• Experience with Python-based API ingestion workflows.
• Familiar with GitHub, Postman, Linux, Azure.
• Strong analytical and communication skills.

Responsibilities

• Design dimensional models using Kimball and star/snowflake schemas.
• Model core banking data: accounts, transactions, profiles, GL.
• Build dbt pipelines; integrate external sources.
• Create modular dbt models with data quality checks.
• Collaborate with Python developers on API ingestion.
• Develop advanced SQL transformations; optimize Snowflake workloads.
• Support Snowflake performance: warehouses, clustering, tuning.
• Implement RBAC and data security practices.
• Work with Linux/Azure environments; use GitHub and Postman.

Skills

Snowflake

SQL

dbt

Dimensional modeling

Kimball

SCDs

RBAC

Linux

Azure

Postman

GitHub

Python APIs

Tools

dbt

Python

GitHub

Postman

Job description

Job Type: Contract - 12+ Months (potential for extension)

Work Arrangement: Onsite preferred; local candidates strongly preferred

Position Overview

We are seeking an experienced Snowflake Data Engineer to support the ingestion, modeling, and transformation of core financial data into Snowflake.

The ideal candidate will have strong experience with Snowflake, SQL, dbt, dimensional data modeling, and financial/core banking data. You will work with data engineers and software developers to transform raw data and third-party API payloads into production-ready dimensional models and a unified enterprise analytics layer.

Key Responsibilities

• Design and develop dimensional data models using Kimball methodology, including star and snowflake schemas.
• Model core banking data including accounts, transactions, customer profiles, and general ledger information.
• Develop dbt transformation pipelines to integrate and append data from external and third-party sources.
• Build, test, document, and maintain modular dbt models with automated data quality checks.
• Work with Python developers to process, flatten, and model raw JSON and relational API payloads.
• Develop complex SQL transformations and optimize Snowflake workloads for analytics.
• Support Snowflake performance optimization, including virtual warehouses, clustering, query optimization, and efficient data processing.
• Implement and maintain appropriate role-based access controls (RBAC) and data security practices.
• Collaborate with software and data engineering teams working across Linux and Azure environments.
• Use GitHub for source control and Postman for API testing and validation.

Required Qualifications

• Hands-on experience with core banking systems or similar financial data platforms.
• Strong expertise in Snowflake architecture and advanced SQL.
• Experience working with semi-structured data, including JSON and Snowflake VARIANT data types.
• Proven experience developing production-grade dbt models, tests, and documentation.
• Strong understanding of dimensional data modeling, including Kimball methodology and Slowly Changing Dimensions (SCDs).
• Experience working with financial or banking data is highly preferred.
• Experience collaborating with Python-based API ingestion workflows.
• Familiarity with GitHub, Postman, Linux, and Azure environments.
• Strong analytical, problem-solving, and communication skills.

Preferred Experience

• Experience with Jack Henry or comparable core banking platforms.
• Experience integrating loans, deposits, digital banking, compliance, or other financial data sources.
• Experience building enterprise analytics/data warehouse layers in Snowflake.
• Experience optimizing Snowflake environments for large-scale analytical workloads.

Candidates with strong Snowflake + dbt + financial/core banking data experience are encouraged to apply.