Senior Data Engineer

Position: Python/Pyspark Data Engineer Location: Remote or Charlotte Duration: 18 mo contract to hire extending Pay: 60-65/hr Required Skills & Experience • 10+ years experience in Data Engineering • Strong hands-on experience with AWS (S3, Lambda, Glue) • Proficiency in Python, including PySpark • Solid background in ETL development and data warehousing concepts • Experience building or working with APIs (FastAPI preferred, but equivalent experience is acceptable) • Ability to clearly walk through past projects and explain design decisions, trade-offs, and outcomes • Exposure to RAG (Retrieval-Augmented Generation) or AI/ML implementations within data engineering (approx. 5% of role) • Can include personal projects Nice to Have • Experience integrating data pipelines with AI or analytics use cases • Familiarity with modern data platforms and orchestration tools What We’re Looking For • Strong problem-solving skills and a practical, hands-on mindset • Clear communicator who can explain technical work to both technical and non-technical audiences • Someone who can confidently walk us through a real project they’ve built or contributed to • Someone who is enthusiastic about new tools and incorporating AI into their skillset Job Overview We are looking for a skilled Data Engineer with a strong AWS and Python background to help design, build, and maintain scalable data pipelines and APIs. This role is ideal for someone who enjoys working across data ingestion, transformation, and delivery, and who can clearly explain their work through real project experience. Key Responsibilities • Design, build, and maintain ETL pipelines using AWS services such as S3, Lambda, and Glue • Develop and optimize data processing workflows using Python and PySpark • Build and support APIs (preferably using FastAPI) to expose data to downstream systems • Work with data warehouse architectures, ensuring data quality, reliability, and performance • Collaborate with cross-functional teams to understand data requirements and translate them into scalable solutions • Participate in code reviews and contribute to best practices in data engineering