Hybrid Data Engineer

Job ID: NC-797603  Hybrid/Local Data Engineer (15+) with ETL/Snowflake, Python/PyPI, SQL, cybersecurity, DevOps, public-cloud, AWS experience Location: Raleigh, NC (NCDPI) Duration: 11 Months ***The manager would like the candidate to be onsite regularly throughout the position. The amount of onsite time per week will be negotiable. However, weekly onsite time will be required. Skills: Demonstrated mid-level+ experience in data engineering, with an emphasis on data quality assurance and ETL processes. Required 5 Years Expertise in Python, PyPI, and SQL Required 5 Years Demonstrate a strong understanding of cybersecurity principles related to code development, DevOps, data access, and fundamental cybersecurity. Required 5 Years Understanding of fundamental public-cloud capabilities. Required 5 Years Proven capacity to comprehend business needs and convert them into technical requirements. Required 5 Years Description: Typical Experience: 60-84 months Job Summary – Data Engineer: We are seeking a skilled mid-level+ Data Engineer to join our team and focus on quality assurance, quality checking, and ETL processes. The successful candidate will be responsible for ensuring the integrity and accuracy of data transferred from a shared file transfer service to an S3 bucket and subsequently into and through our Snowflake data platform. This data will be utilized by downstream applications and reporting systems. These applications and the corresponding consumed data are critical to business process execution. Key Responsibilities: Quality Assurance & Quality Checking: Implement and maintain data quality to ensure the accuracy and reliability of data throughout the ETL process. ETL Processes: Design, develop, and optimize ETL workflows to efficiently transfer data from file transfer services to S3 buckets and Snowflake. Data Integration: Ensure seamless data integration into data platform, enabling efficient consumption by downstream applications and reporting tools. Data Quality Management: Address data quality challenges, including inconsistencies in source data that do not meet ingestion requirements, which can lead to load failures or data backouts. Collaboration: Work closely with business owners, data analysts, business intelligence teams, and other stakeholders to understand data requirements and deliver high-quality data solutions. Monitoring & Troubleshooting: To preserve data flow and integrity, monitor pipelines, identify issues, and implement solutions. Harry Lead Recruiter Email: [Upgrade to PRO to see contact]