Join Deloitte's Technology & Transformation practice as a Senior Consultant specializing in Python. This role focuses on driving the design, development, and delivery of robust backend systems. You will leverage your expertise in Python (Django/Flask), AWS, and Apache Kafka to build scalable and reliable solutions.

This is a hands-on technical leadership position where you'll not only write high-quality code but also set technical direction, mentor fellow engineers, and ensure the overall quality and scalability of the services developed by your team. You'll be instrumental in defining architecture, enforcing engineering standards, and fostering the growth of team members.

Lead the technical design and architecture of backend services, APIs, and data pipelines, ensuring scalability, reliability, and maintainability.

Take ownership of end-to-end feature delivery, from initial requirements and design through implementation, code review, testing, deployment, and production support.

Contribute actively through hands-on engineering, writing excellent, well-tested Python code and establishing high standards for the team.

Mentor and guide engineers through code reviews, pairing sessions, and by actively supporting their career development and skill enhancement.

Define and enforce best practices in coding standards, testing, CI/CD, observability, and security.

Collaborate with product managers, designers, and stakeholders to translate business objectives into effective technical roadmaps.

Design and operate event-driven systems using Kafka for dependable and scalable inter-service communication.

Manage cloud infrastructure and application deployments on AWS, focusing on cost optimization, performance, and resilience.

Identify and address technical debt, performance bottlenecks, and resolve production incidents.

A minimum of 8 years of professional software engineering experience with a proven history of delivering and maintaining production-grade systems is required.

Demonstrate a clear career progression from an individual contributor to a senior or lead engineering role, having owned significant features or systems.

Possess a strong foundation in software design principles, data structures, algorithms, API design, and system architecture.

Experience across the entire software development lifecycle, including requirements gathering, design, implementation, testing, deployment, and maintenance.

Core Python expertise with at least 5 years of hands-on application development experience, including deep language and ecosystem knowledge.

Production experience with web frameworks like Django or Flask, including building and maintaining RESTful APIs and web services.

Proficiency in relational databases (e.g., PostgreSQL, MySQL) and ORMs such as Django ORM or SQLAlchemy, with an emphasis on query optimization.

Strong testing discipline using frameworks like pytest or unittest, and experience integrating tests into CI pipelines.

Experience profiling, debugging, and scaling Python services under load.

Practical, developer-focused experience building and deploying applications on AWS, using core services such as EC2, S3, Lambda, RDS, IAM, and API Gateway.

Experience with containerization using Docker and familiarity with ECS/EKS or similar platforms.

Understanding of AWS monitoring, logging, and alerting tools (e.g., CloudWatch).

A minimum of 3 years leading engineering teams or squads is essential.

Proven ability to mentor and grow engineers through effective code reviews, pairing, and structured feedback.

Experience in planning, estimating, and delivering projects on time, managing scope and technical trade-offs.

Excellent communication skills, capable of explaining complex technical concepts to diverse audiences.

At least 1 year of hands-on experience with Apache Kafka in a production or near-production environment.

Experience designing and implementing Kafka producers and consumers, including topic and partition strategies.

Understanding of event-driven architecture patterns, message ordering, delivery guarantees, and schema management.

Familiarity with handling failure scenarios in Kafka, such as retries and dead-letter queues.