Senior Python Developer

KMM Technologies•Reston, VA

2d•Hybrid

About The Position

The Senior Python Developer will design, develop, test, and implement cloud native applications, data pipelines, and backend services within AWS. The role requires strong Python engineering skills, AWS service expertise, foundational data engineering capabilities, and a disciplined approach to software quality and automation. The ideal candidate will be able to work collaboratively across teams, follow established engineering practices, and deliver secure, scalable, and maintainable solutions.

Requirements

Strong proficiency in Python for backend service development.
Experience with relevant Python libraries such as Pandas, Boto3, and data processing packages.
Proficiency with automated testing using PyTest, including fixtures, mocking, and parameterization.
Understanding of clean code principles, error handling, type hints, and maintainable design.
Hands-on experience building RESTful APIs using Flask, Django, or FastAPI.
Knowledge of authentication and authorization mechanisms (JWT, OAuth2).
Experience implementing API versioning, request validation, and structured error handling.
Understanding of performance considerations such as throughput, latency, and caching.
Practical experience with key AWS services including: Lambda, S3, Step Functions, Glue, EC2, ECS/Fargate, RDS, Redshift, CloudWatch.
Ability to design event driven, serverless, and containerized architectures.
Familiarity with distributed systems patterns such as retries, dead letter queues, and idempotent operations.
Experience monitoring applications via CloudWatch metrics, logs, and alarms.
Hands-on experience with GitLab for version control and CI/CD pipeline development.
Experience using Terraform (or similar) for infrastructure as code.
Proficiency with Docker for containerized application development.
Ability to use shell scripting and AWS CLI for operational automation.
Familiarity with Agile development workflows (Jira, Confluence).
Demonstrate familiarity with GitHub Copilot or comparable AI assisted development tools.
Understand organizational expectations around responsible AI usage, including validation, security, and compliance.
Practical exposure to foundational data engineering concepts.
Experience building or maintaining data pipelines using AWS Glue, PySpark, or Lambda based ETL flows.
Working knowledge of SQL and relational databases (e.g., Postgres, Aurora, or MySQL).
Ability to optimize SQL queries (joins, aggregations, window functions).
Understanding of data modeling, data validation, and schema evolution.
Familiarity with Redshift ingestion patterns (e.g., COPY operations) and performance optimization.
Experience working with data stored in S3, including partitioning, data lifecycle considerations, and file formats (Parquet/JSON).
Ability to communicate effectively with technical and non technical stakeholders.
Strong analytical and problem solving capability.
Demonstrated ability to navigate, understand, and improve existing codebases.
Understanding of design patterns and architectural principles.
Focus on reliability, scalability, automation, and long term maintainability.
Ability to collaborate in Agile teams and provide high quality documentation.

Nice To Haves

Basic familiarity with Angular, particularly for integrating backend APIs.
Ability to read and modify UI components when needed.

Responsibilities

Design, develop, test, and implement cloud native applications, data pipelines, and backend services within AWS.
Utilize strong Python engineering skills for backend service development.
Leverage AWS service expertise for cloud-native solutions.
Apply foundational data engineering capabilities to build and maintain data pipelines.
Maintain a disciplined approach to software quality and automation.
Collaborate effectively across teams.
Follow established engineering practices.
Deliver secure, scalable, and maintainable solutions.
Build RESTful APIs using Flask, Django, or FastAPI.
Implement API versioning, request validation, and structured error handling.
Design event-driven, serverless, and containerized architectures.
Monitor applications via CloudWatch metrics, logs, and alarms.
Develop CI/CD pipelines using GitLab.
Use Terraform for infrastructure as code.
Develop containerized applications using Docker.
Automate operations using shell scripting and AWS CLI.
Participate in Agile development workflows.
Use AI assisted development tools responsibly to accelerate development, maintain code correctness, generate and refine tests, improve readability, documentation, and assist with refactoring, code reviews, and exploratory coding.
Build or maintain data pipelines using AWS Glue, PySpark, or Lambda based ETL flows.
Optimize SQL queries.
Work with data stored in S3, including partitioning, data lifecycle considerations, and file formats.
Integrate backend APIs with front-end frameworks like Angular.
Read and modify UI components when needed.
Communicate effectively with technical and non-technical stakeholders.
Demonstrate analytical and problem-solving capabilities.
Navigate, understand, and improve existing codebases.
Apply design patterns and architectural principles.
Focus on reliability, scalability, automation, and long-term maintainability.
Collaborate in Agile teams and provide high-quality documentation.