Must Have Skills:
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.
- Strong proficiency in Python for backend service development with 8-10 years of experience
- 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
API Engineering
- 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
AWS Cloud Services
- 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
Infrastructure, DevOps, & Tooling
- 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)
AI Assisted Development Tools (Required Competency)
To support modern engineering productivity and code quality standards, candidates must:
- Demonstrate familiarity with GitHub Copilot or comparable AI assisted development tools
- Use AI tools responsibly to:
- Accelerate development while maintaining code correctness
- Generate and refine unit tests and integration test scaffolding
- Improve readability, documentation, and boilerplate reduction
- Assist with refactoring, code reviews, and exploratory coding
- Understand organizational expectations around responsible AI usage, including validation, security, and compliance
- This is a mandatory requirement, not a bonus skill.
Data Engineering Skills (Required)
Candidates are expected to have practical exposure to foundational data engineering concepts, including:
- 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)
Front-End Exposure
- Basic familiarity with Angular, particularly for integrating backend APIs
- Ability to read and modify UI components when needed
Soft Skills & Engineering Mindset
- 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
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