Role: Data Engineer (Python, SQL, Snowflake, ETL, AWS S3)
Start Date: 03/30/2026
End Date: 03/26/2027
Location: Raleigh, NC
Interview Type: Webcam or In-Person
Complete DescriptionOnly qualified Data Engineer candidates located in the Raleigh, NC area will be considered, as this position requires an onsite presence.Required Skills- 7+ years of experience in Data Engineering, with a strong focus on ETL development and data quality assurance.
- 5+ years of hands-on experience with Python, PyPI, and SQL.
- Strong understanding of cybersecurity principles related to secure code development, DevOps, data access, and data protection.
- Solid knowledge of public cloud technologies, including AWS services such as S3.
- Proven ability to translate business requirements into scalable technical solutions.
- Excellent analytical and problem-solving skills.
- Strong communication and collaboration abilities, with experience working across cross-functional teams.
- Experience working in Agile environments, with a demonstrated ability to deliver high-quality solutions and achieve project objectives.
Position OverviewThe client is seeking a skilled mid-level to senior Data Engineer to support data quality assurance, ETL development, and data integration initiatives. The selected candidate will ensure the accuracy, integrity, and reliability of data as it moves from a secure file transfer service into AWS S3 and through the Snowflake data platform. This data supports critical downstream applications, analytics, and reporting systems that are essential to core business operations.
Key Responsibilities- Data Quality Assurance: Develop and maintain data quality validation processes to ensure the accuracy, consistency, and reliability of data throughout the ETL lifecycle.
- ETL Development: Design, build, optimize, and maintain ETL pipelines to efficiently transfer data from secure file transfer services to AWS S3 and Snowflake.
- Data Integration: Ensure seamless integration of data into the Snowflake platform, enabling reliable access for downstream applications and reporting solutions.
- Data Quality Management: Identify and resolve data quality issues, including source data inconsistencies that may cause ingestion failures, processing errors, or data rollbacks.
- Collaboration: Partner with business stakeholders, data analysts, business intelligence teams, and technical teams to gather requirements and deliver high-quality data solutions.
- Monitoring & Troubleshooting: Continuously monitor data pipelines, proactively identify issues, troubleshoot failures, and implement solutions to maintain data integrity and uninterrupted data flow.
