Data Engineer with Snowflake and DBT || W2 Only

Job Opportunity:Data Engineer Location:Hybrid 3 days Oaks, PA. Duration: Contract Key Responsibility What you will do: • Design, develop, and maintain CI/CD pipelines in GitLab for automated deployment of data platform components including dbt transformations, Airflow DAGs, and Snowflake database objects across development, QA, UAT, and production environments. • Implement and optimize blue-green deployment patterns and environment promotion strategies to ensure zero-downtime releases and safe rollback capabilities for the Data Cloud infrastructure. • Build automated testing integration within deployment pipelines to validate data transformations, Snowflake stored procedures, functions, and materialized views before production promotion. • Collaborate with QA teams to integrate validation frameworks and testing portals into the CI/CD workflow, ensuring data quality gates are enforced at each stage of the deployment process. • Transition into hands-on data engineering work developing Snowflake data shares for cross-functional data access and building reporting analytics warehouses that consolidate data from multiple source systems including Investran/KYC, Geneva RSL, and Investier. • Develop and optimize Snowflake objects including views, stored procedures, functions, and materialized views to support reporting and analytics requirements while maintaining performance and cost efficiency. • Work within the Data Cloud/Azure infrastructure team to deploy and manage data pipeline components, coordinating with parallel teams handling Snowflake extracts and Reporting/PowerBI workstreams. What we need from you: • Strong hands-on experience with GitLab CI/CD pipeline development and deployment automation, including YAML configuration, pipeline orchestration, and environment management strategies. • Solid understanding of DevOps practices and principles including infrastructure as code, automated testing, continuous integration/continuous deployment, and version control workflows using Git. • Proficiency in SQL for writing queries, stored procedures, and functions with the ability to implement data transformations based on provided specifications and requirements. • Working knowledge of Snowflake architecture and database objects including tables, views, materialized views, stored procedures, functions, and data sharing capabilities. • Experience with dbt (data build tool) for implementing SQL-based data transformations, including model development, testing, documentation, and deployment patterns based on existing designs. • Familiarity with Apache Airflow for workflow orchestration, including DAG development, task dependencies, and scheduling strategies for data pipeline automation. • Good Python scripting skills for automation tasks, data processing, and integration work between various platform components. • Demonstrated ability to work in Agile environments and collaborate effectively with cross-functional teams including QA engineers, data analysts, business stakeholders, and infrastructure teams. Additional knowledge/experience desired: • Experience working with Azure cloud infrastructure and services, particularly as they relate to data platform deployments and CI/CD tooling integration. • Knowledge of snowsql scripting and command-line interfaces for Snowflake automation and deployment scripting. • Understanding of testing methodologies for data pipelines including unit testing, integration testing, and user acceptance testing coordination. • Exposure to reporting and visualization tools such as PowerBI or similar business intelligence platforms. What we would like from you: • Experience managing deployments across complex multi-environment landscapes with clear separation between development, QA, UAT, and production tiers. • Track record of implementing automated testing and validation within CI/CD pipelines to catch issues early and maintain high data quality standards. • Strong problem-solving abilities with a mindset toward building reusable, maintainable automation solutions that can scale with project growth. • Excellent communication skills and the ability to document technical processes clearly for knowledge transfer to QA teams and other stakeholders. • Willingness to grow from a DevOps-focused role into broader data engineering responsibilities as the platform matures and pipeline automation stabilizes. • Self-motivated approach to learning new technologies and adapting to the evolving needs of a large-scale data migration and analytics platform project.