Description

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