Delivery Manager-Full Stack Data Engineer

Job Purpose

Architect and deliver scalable, cloud-native Full Stack and Data Engineering solutions by providing technical leadership across frontend, backend, data, and cloud platforms. Responsible for defining end-to-end architecture, developing critical solution components, establishing engineering best practices, and ensuring secure, reliable, and high-performing systems. Drive the design and implementation of React-based applications, Python/FastAPI services, Snowflake data platforms, and Azure cloud solutions while mentoring engineering teams and translating business requirements into pragmatic technical outcomes.

Key Responsibilities

• Design and implement end-to-end solution architecture spanning React frontends, Streamlit applications, FastAPI services, Snowflake data platforms, and Azure cloud infrastructure.
• Lead architecture decisions across application, data, integration, security, and cloud domains while remaining actively involved in software development.
• Develop and review production-grade Python, FastAPI, React, and Node.js code, ensuring adherence to architectural and coding standards.
• Design, build, and optimize scalable RESTful APIs, incorporating authentication, authorization, caching, observability, and performance best practices.
• Architect and implement modern data platforms using Snowflake, DBT, Airflow, and Azure services to support ingestion, transformation, and analytics workloads.
• Establish Snowflake data architecture, including database design, role-based security, warehouse optimization, data loading frameworks, and cost governance.
• Design and manage Azure SQL solutions, ensuring high availability, performance optimization, security, and disaster recovery capabilities.
• Build and maintain CI/CD pipelines using GitHub Actions, automating application, database, and data pipeline deployments across environments.
• Define Azure cloud architecture covering networking, identity management, Key Vault integration, monitoring, container platforms, and cost optimization.
• Implement engineering best practices including automated testing, data quality validation, observability, logging, monitoring, and documentation standards.
• Drive containerization and deployment strategies using Docker, AKS, and Azure Container Apps.
• Collaborate with business stakeholders to translate functional requirements into scalable technical solutions and provide architectural recommendations.
• Lead design reviews, code reviews, and technical governance activities while mentoring development and data engineering teams.
• Support AI/LLM integration initiatives, enabling intelligent data products and business process automation.
• Troubleshoot complex production issues across application, API, data pipeline, cloud, and database environments.
  
  

• Bachelor’s or Master’s degree in computer science, Information Systems, Engineering, MCA, or a related discipline.
• Proven expertise in designing and delivering end-to-end Full Stack and Data Engineering solutions across frontend, backend, data, and cloud platforms.
• Strong hands-on experience with React.js, Streamlit, Python, FastAPI, and Node.js for developing scalable enterprise applications.
• Extensive knowledge of Snowflake architecture, DBT data transformation, Airflow orchestration, and modern ETL/ELT frameworks.
• Deep understanding of Microsoft Azure services, including networking, security, identity management, Key Vault, monitoring, and cost optimization.
• Expertise in designing secure, high-performance REST APIs, microservices, and cloud-native architectures.
• Strong DevOps and CI/CD experience using GitHub Actions, Docker, AKS, and container-based deployment strategies.
• Skilled in implementing observability frameworks, application monitoring, logging, distributed tracing, and production support practices.
• Experienced in establishing engineering standards, code quality processes, data governance, testing frameworks, and architectural best practices.
• Strong leadership capabilities with expertise in mentoring engineering teams, conducting design reviews, and driving technical decision-making.
• Adept at stakeholder management, translating business requirements into scalable technical solutions, and integrating AI/LLM capabilities into enterprise data products.