Data Engineer

Charlotte, NC (Onsite)

6 Months+

Payrate: $75/hr to 80/hr C2C submission rate

Required Skills & Qualifications

Data engineering, backend development, or software engineering, with demonstrated hands-on development experience.
Strong hands-on experience with AWS data and cloud services, including services such as S3, Glue, EMR, Lambda, and Redshift.
Proven experience developing and maintaining production-grade APIs and backend services using Java, with a strong understanding of RESTful API design, integration patterns, and error handling.
Strong programming skills in Python, with hands-on experience building reusable modules, backend components, or API integrations.
Hands-on experience with PySpark and distributed data processing, including developing scalable data transformations and optimising data pipelines.
Proficiency in SQL, including complex queries, data transformations, and query performance optimisation.
Strong understanding of object-oriented programming, software design principles, data structures, and maintainable application architecture.
Practical exposure to MCP, LLMs, generative AI, or AI application integration, with an understanding of how AI capabilities can be integrated into APIs and enterprise workflows.
Experience with API testing, debugging, authentication and authorisation, and application integration.
Familiarity with Git, CI/CD pipelines, automated testing, and Agile development practices.
Strong problem-solving, communication, and collaboration skills, with the ability to work independently on complex technical requirements.
Preferred Qualifications (Nice to Have)

Experience building AI-enabled applications, LLM-powered workflows, or MCP-based integrations.
Exposure to LLM orchestration frameworks, tool calling, retrieval-augmented generation (RAG), or enterprise AI integration patterns.
Experience with AWS-native AI services and deployment of AI-enabled applications in cloud environments.
Familiarity with API gateways, containerisation, microservices architecture, and application observability.
Experience with data warehousing, distributed computing, Spark performance tuning, and data quality frameworks.

Job Responsibilities

Key Responsibilities

Design, develop, and optimise scalable data pipelines and data processing workflows using Python, PySpark, and SQL.
Develop, enhance, and maintain production-grade APIs and backend services using Java and Python, with a strong focus on API design, integration, performance, and reliability.
Build and integrate RESTful APIs and microservices to support data-driven applications and enterprise integrations.
Leverage AWS services such as S3, Glue, EMR, Lambda, Redshift, and other relevant cloud-native services to build and manage scalable data engineering solutions.
Work with Model Context Protocol (MCP), Large Language Models (LLMs), and generative AI technologies to develop or integrate AI-enabled applications and workflows.
Implement reusable, maintainable, and scalable code using object-oriented programming principles, design patterns, and established engineering best practices.
Write and optimise complex SQL queries and implement efficient ETL/ELT processes for large-scale data transformations.
Collaborate with data engineers, application developers, architects, data scientists, and business stakeholders to translate requirements into robust technical solutions.
Troubleshoot and resolve issues across APIs, data pipelines, integrations, and cloud-based applications.
Participate in code reviews, automated testing, CI/CD implementation, performance tuning, and production support while adhering to engineering standards.