Senior Technical Architect – Python

Roles & Responsibilities

Senior Technical Architect – Python

Key Responsibilities

• Technical Leadership: Drive architecture decisions and design scalable, secure, and high-performing solutions.
• Project Management: Plan, execute, and deliver projects within timelines and budgets, ensuring stakeholder alignment.
• Develop, test, and maintain enterprise-grade applications using Python.
• Collaborate with cross-functional teams to define requirements and deliver robust solutions.
• Write clean, reusable, and maintainable code following best practices.
• Ensure adherence to SDLC, quality standards, and compliance requirements.
• Optimize applications for performance, scalability, and security.
• Mentor junior developers and foster a culture of technical excellence
• Stay updated with emerging technologies and recommend adoption where relevant.
• Ensure timely delivery of features through iterative development and continuous feedback.
• Collaborate with Product Owners and stakeholders to prioritize backlog items effectively.
• Promote Agile principles and best practices across the team to enhance productivity and adaptability.

Required Skills & Qualifications

• Completed Degree qualification - Mandatory
• 12–15 years of industry experience, with at least 8+ years in Python development.
• Strong expertise in Python frameworks (Django, Flask, FastAPI) and libraries (NumPy, Pandas).
• Proven experience in solution architecture and designing distributed systems.
• Hands-on experience with cloud platforms (AWS, Azure, GCP).
• Proficiency in SQL and NoSQL databases.
• Strong understanding of Data Engineering, ETL principles, and API design.
• Experience in DevOps practices, CI/CD pipelines, and containerization (Docker, Kubernetes).
• Excellent problem-solving skills, analytical thinking, and ability to handle complex technical challenges.
• Strong communication and stakeholder management skills.

Nice to Have

• Experience with Microservices architecture and event-driven systems.
• Knowledge of Big Data technologies and data pipelines.
• Exposure to Agile methodologies and Scrum leadership