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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - **Company:** The Smart - **Location:** Morrisville, NC, United States (Remote available) - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Airflow, Microsoft Azure, Big Data, Databases, Continuous Integration, Information Engineering, Data Integration, Extract Transform Load (ETL), Data Transformation, Data Migration, Data Warehousing, Relational Databases, Python (Programming Language), Microsoft SQL Server, Software Tools, Standard Sql, SAP NetWeaver Data Management, SQL Databases, Systems Integration, Enterprise Data Management, File Transfer Protocol (FTP), Snowflake, Microsoft Fabric, Data Pipelines, Service Stack - **Published:** November 4, 2026 - **Apply:** https://www.thejobnetwork.com/job/5b118f43-ec6e-4df3-b317-b21caec92fbd/senior-data-engineer ## About the Role * Strong hands-on Snowflake experience. * Strong hands-on dbt experience. * Strong Python coding skills. * Strong SQL and relational database experience. * Senior-level data engineering or data warehouse development experience. * Experience developing and supporting enterprise data pipelines, integrations, and data feeds. * Experience with data warehouse modernization, migration, or large-scale data movement. * Ability to understand existing data processes and translate them into modern engineering patterns. * Ability to provide technical mentorship, guidance, and knowledge transfer. * Experience using AI-assisted engineering tools professionally, with examples of improved development productivity or technical delivery. * Strong technical judgment, troubleshooting, and problem-solving capabilities. * Ability to independently own technical deliverables and adopt new technologies quickly. * Strong communication and collaboration skills. Highly Preferred Qualifications * Airflow on Fabric experience. * IBM StreamSets experience. * Experience with Microsoft Fabric. * Experience migrating SQL Server or traditional relational data into Snowflake. * Experience orchestrating dbt, StreamSets, database, or cloud-storage workflows. * Experience modernizing or replacing legacy ETL/data integration platforms. * Experience with SAP Data Services or similar enterprise ETL technologies. * CI/CD implementation within data engineering environments. * Experience establishing engineering standards and helping teams adopt new technologies. Nice to Have * Azure-based data or storage services. * Experience working alongside managed service providers during technology implementations. * Experience introducing new engineering tools or practices within an established organization. * Previous experience serving as a technical mentor, lead, or senior resource for a data engineering team. What Makes Someone Successful in This Role? This position is particularly well suited for someone who enjoys being the person a team can turn to when adopting unfamiliar technology. You should be comfortable building solutions yourself, but equally comfortable sitting alongside other engineers, explaining how and why something works, establishing repeatable patterns, and helping the team become self-sufficient. We want someone who stays current with emerging technologies, actively uses AI to improve engineering productivity, and can explain the practical results of those improvements. The goal isn't simply to bring in someone who can execute Snowflake and dbt work independently. We need someone who can help an established data warehouse team retool its skills, establish confidence with the new platform, and ultimately take technical ownership of the environment. ## Description We are seeking a Senior Data Engineer to play a key role in a major data modernization initiative as the organization transitions its data warehouse and integration environment to Snowflake, dbt, Microsoft Fabric/Airflow, and IBM StreamSets. This is more than a traditional development position. We are looking for someone who can be hands-on technically while also providing mentorship, knowledge transfer, and technical direction to an established data warehouse team as they adopt this new technology stack. Snowflake, dbt, SQL, and Python coding skills are required for this position. Experience with Airflow on Fabric and/or IBM StreamSets is highly preferred and will be particularly valuable as the organization replaces legacy SAP Data Services processes and modernizes its data integration and orchestration capabilities. The right person will become a key technical resource within the data engineering environment-helping establish best practices, guiding engineers through the transition, and taking increasing ownership as knowledge is transferred from existing subject-matter experts. We are particularly interested in engineers who are actively using AI-assisted development tools to improve productivity, automate processes, and accelerate technical delivery. What You'll Do * Design, develop, and support data engineering solutions within a Snowflake data warehouse environment. * Build and maintain data transformations and workflows using dbt. * Write and maintain Python code supporting data engineering, automation, and integration processes. * Support migration from existing SQL Server and legacy data warehouse environments into Snowflake. * Work with IBM StreamSets to support data migration, integration, FTP, and enterprise data-feed processes. * Use Airflow on Fabric to orchestrate data workflows, scheduling, StreamSets jobs, dbt jobs, and related processes. * Help transition legacy data integration processes away from SAP Data Services and into the modern data engineering platform. * Build and support enterprise data feeds integrating information from multiple systems. * Implement and improve CI/CD practices across data engineering workflows. * Establish development standards, reusable patterns, and best practices across the new technology stack. * Provide hands-on technical mentorship and knowledge transfer to existing data warehouse engineers. * Initially partner with key members of multiple data teams and progressively help expand knowledge across a broader group of approximately 15 team members. * Help existing engineers transition their skills into Snowflake, dbt, Airflow on Fabric, and StreamSets. * Collaborate with internal technical resources, IBM, and subject-matter experts during implementation and knowledge transfer. * Use AI-assisted engineering tools to improve development, troubleshooting, testing, and productivity. * Troubleshoot complex data integration, transformation, orchestration, and migration issues. * Take increasing technical ownership of the modern data engineering ecosystem as the implementation matures.