Data Engineering - W2

Role Overview

We are seeking a hands-on Data Engineer to support a major data modernization initiative as the

organization transitions its data warehouse and integration environment into Snowflake, dbt, Microsoft

Fabric, Apache Airflow, and IBM Stream Sets.

We're looking for someone who is technically strong, quick to adopt new technologies, and actively using

AI tools to improve engineering productivity. The ideal candidate can provide examples of how they've

used AI in a corporate environment to accelerate development, automate processes, or improve

technical solutions.

This role will focus on building and supporting the new data engineering environment while working

alongside internal teams and IBM as the organization transitions away from legacy technologies.

What You'll Do

• Design, develop, and support data engineering solutions within Snowflake.

• Build and maintain data transformations and workflows using dbt.

• Support migration from SQL Server and legacy data warehouse environments into Snowflake.

• Develop and maintain enterprise data pipelines, integrations, and data feeds.

• Work with IBM StreamSets to support data integration, migration, and enterprise data-feed

processes.

• Use Apache Airflow and/or Microsoft Fabric to support data orchestration and scheduling.

• Help modernize legacy ETL processes, including those currently supported by SAP Data Services.

• Implement and improve CI/CD practices across data engineering workflows.

• Use AI tools to accelerate development, troubleshooting, testing, and engineering productivity.

• Identify opportunities to automate manual processes and improve overall delivery.

• Work with IBM and internal engineering teams as the new technology environment is

established.

• Troubleshoot data integration, transformation, and orchestration issues.

• Contribute to development standards and best practices as the environment continues to

Evolve.

Required Qualifications

• Strong hands-on experience with Snowflake.

• Strong hands-on experience with dbt.

• Strong SQL and relational database experience.

• Experience building and maintaining enterprise data pipelines and integrations.

• Experience with data warehouse modernization or migration initiatives.

• Strong technical judgment and ability to work independently.

• Demonstrated ability to quickly adopt and implement new technologies.

• Experience using AI-assisted engineering tools in a professional environment, with examples of

how they've improved development productivity or technical delivery.

• Strong problem-solving and communication skills.

Highly Preferred Qualifications

• Apache Airflow experience.

• IBM StreamSets experience.

• Microsoft Fabric experience.

• CI/CD implementation within data engineering environments.

• Experience migrating data from SQL Server or other traditional relational environments into

Snowflake.

• Experience orchestrating dbt, StreamSets, or cloud-based data workflows.

• Experience modernizing legacy ETL and data integration platforms.

Nice to Have

• Python development experience.

• Azure-based data or storage services.

• SAP Data Services experience.

• Experience working alongside managed service providers during a technology implementation.

• Experience introducing new development tools or engineering practices within an established

organization