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.

Key competencies

Bachelors or Masters 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.

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