MongoDB Developer

• Strategic Architecture Design: Apply your Python expertise to lead the conceptualization and architecture of scalable, efficient, and resilient infrastructure automation solutions, with a focus on asynchronous and event-driven patterns.
• DevOps Practices Implementation: Champion the implementation of DevOps practices and Python-centric CI/CD pipelines to enhance automation workflows.
• Infrastructure Automation Expertise: Leverage expert-level Python skills to develop intricate automation solutions for network, firewalls, and other IT infrastructure components, focusing on advanced API integrations following RESTful and gRPC principles.
• Technical Mentorship: Act as the lead Python authority, mentoring and providing expert technical guidance to the development team, fostering a culture of continuous improvement and innovation in Python development.
• Container Management Leadership: Offer strategic guidance and establish best practices for container management with Docker and Kubernetes, particularly in Python-based applications.
• Comprehensive Documentation: Create and sustain detailed documentation for Python code, system architecture, and implementation processes.
• Cross-Functional Systems Integration: Collaborate with cross-functional teams to ensure seamless integration with existing systems, including the Elastic software stack and ServiceNow.
• ServiceNow Integration Strategy: Drive the strategic integration of Python-based automation tools with ServiceNow, ensuring alignment with business processes and requirements.

• Python Libraries & Frameworks: Proven, hands-on experience with a broad range of technologies essential for high-performance data operations and API development, including:

• Data Modeling & ORMs/ODMs (MongoDB Focus): Deep, practical knowledge of PyMongo for direct MongoDB interaction. Extensive experience with MongoEngine and/or Pydantic with MongoDB integration for robust data modeling, validation, and schema enforcement within a NoSQL context. Understanding of best practices for embedding and linking documents.
• Web & API Frameworks: Proficiency in building high-performance, scalable, and secure RESTful and/or GraphQL APIs using FastAPI (highly preferred for modern async APIs), Flask (for lightweight services), and/or Django REST Framework (for comprehensive web applications with ORM capabilities). Strong experience with the Requests library for client-side integration and API consumption.

• RPC Frameworks & Protocols: Hands-on experience designing and implementing services using gRPC for efficient inter-service communication.
• Data Handling & Transformation: Expert-level proficiency with Pandas for data manipulation and analysis. Strong experience with PyYAML, lxml, and native JSON handling for configuration and data interchange. Understanding of data serialization/deserialization best practices.
• Task Queues & Concurrency: Celery for distributed task processing and background jobs. Experience with message brokers like RabbitMQ or Redis.
• Testing Frameworks & Methodologies: Pytest (with fixtures, parametrization, and async testing capabilities), Unittest, and Mock for comprehensive unit, integration, and end-to-end testing. Adherence to Test-Driven Development (TDD) principles.

• API Design Principles & Best Practices: Deep understanding of RESTful API design principles, HATEOAS, versioning strategies, error handling, and documentation (e.g., OpenAPI/Swagger).
• Security Best Practices (API & MongoDB): Implementation of API security measures (e.g., OAuth2, JWT, API Keys, rate limiting), and MongoDB security (authentication, authorization with roles, TLS/SSL, encryption at rest, field-level encryption, audit logging).
• Containerization & Orchestration: Docker for containerizing Python applications and MongoDB instances. Familiarity with Kubernetes for deployment and orchestration.
• Infrastructure as Code & Cloud Platforms: Boto3 (for AWS services), python-terraform (for programmatic Terraform interactions). Experience with deploying and managing MongoDB on cloud platforms (e.g., AWS DocumentDB, MongoDB Atlas, Azure Cosmos DB for MongoDB).
• Version Control: Git (GitHub, GitLab, Bitbucket) for collaborative development and code management.
• CI/CD Pipelines: Experience in setting up and maintaining Continuous Integration and Continuous Delivery pipelines using tools like Jenkins, GitLab CI/CD, GitHub Actions, or CircleCI for automated testing, building, and deployment.
• Configuration Management: Ansible and Terraform for automating infrastructure provisioning and application deployment.
• Monitoring & Logging: Tools like Prometheus, Grafana, ELK stack (Elasticsearch, Logstash, Kibana) for application and database monitoring, logging, and alerting.

• MongoDB Database Expertise:

• Schema Design & Optimization: Expert-level understanding of MongoDB's flexible schema model. Ability to design efficient and scalable document schemas for various application needs, including considerations for embedding, referencing, and denormalization.
• Query Optimization & Indexing: Advanced knowledge of query optimization techniques, including effective use of indexes (single-field, compound, multi-key, text, geospatial), $explain command analysis, and performance monitoring.
• Replication & High Availability: Deep understanding and hands-on experience with MongoDB replica sets for data redundancy and high availability, including primary-secondary architecture, elections, and write concerns.
• Sharding & Horizontal Scaling: Practical experience with MongoDB sharding for horizontal scaling of large datasets, including sharding strategies, shard key selection, and balancing.
• Aggregation Framework: Master proficiency in the MongoDB Aggregation Framework for complex data transformations, reporting, and analytics.
• Database Administration & Monitoring (DevOps Context): Familiarity with mongod administration, mongosh utilities, currentOp for active operations, storage monitoring, and common alerts. Understanding of MongoDB Atlas or self-hosted administration best practices.

Talent Worx