Description

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

Key Skills

Education

Bachelor's degree