DataOrb - Python Developer - Distributed Systems

About DataOrb :

DataOrb is revolutionizing how organizations understand and utilize their customer data.

We enable businesses of all sizes-from ambitious startups to Fortune 500 companies-to unlock insights from their customer interactions across conversational, transactional, and structured datasets.

Founded by veterans from Google, Amazon, Microsoft, and Samsung, we're driven by a shared mission to democratize customer intelligence and make AI accessible to everyone.

The Opportunity :

We are seeking an experienced Python Developer proficient in object-oriented programming, Python development, cloud technologies, database design, and advanced Python concepts.

The ideal candidate will have a foundational understanding of machine learning, with a strong willingness to learn and grow in this domain.

The role involves writing high-quality Python code following SOLID principles and design patterns, as well as guiding and training team members to elevate their coding standards.

Core Responsibilities :

- Architect and develop robust, scalable, and maintainable Python applications following microservice architecture principles

- Demonstrate proficiency in writing multithreaded and parallel processing code for optimizing performance

- Drive the creation of modularized codebase, ensuring reusability and maintainability across projects

- Develop high-quality Python code adhering to SOLID principles and design patterns

- Design and implement scalable solutions leveraging cloud technologies

- Contribute to database design and optimization strategies

- Mentor and guide team members to enhance code quality and best practices

- Collaborate with cross-functional teams to deliver robust and efficient solutions.

- Collaborate closely with stakeholders to understand requirements and translate them into technical solutions

- Drive code reviews and ensure adherence to coding standards, quality, and performance benchmarks

- Research and implement emerging technologies to enhance system efficiency

- Lead initiatives to improve development processes and tools, fostering innovation and productivity

- Foster a culture of continuous learning and improvement within the team

Required Qualifications :

- Full-time hands-on software engineering experience : Minimum 5+ years designing, developing, and delivering high-performance, production-grade applications using Python in complex, distributed systems.

- Expert-level proficiency in Python 3.12+ (or latest stable release)

- Deep understanding of object-oriented programming (OOP), design patterns (e., Singleton, Factory, Strategy), and Python-specific idioms (e. , context managers, decorators, generators, async/await).

- Follows PEP8 standards and best practices for readable, maintainable, and testable code.

- Proficient in type hints (PEP 484), dataclasses, and pydantic for robust, type-safe applications.

- Strong grasp of core engineering concepts :

1. Connection pooling (e. , SQLAlchemy, psycopg2 connection pools)

2. Scalability, throughput optimization, memory management, and profiling using tools like cProfile, line_profiler, memory_profiler.

- Asynchronous programming using asyncio, aiohttp, and event-driven architectures.

- Experience designing distributed systems and microservices using REST APIs, gRPC, or GraphQL.

- Cloud-native development experience, preferably with AWS (Lambda, SQS, SNS, S3, RDS, DynamoDB, SageMaker).

- Strong database expertise :

1. Relational databases (PostgreSQL, MySQL) : query optimization, indexing strategies, schema design, transactions.

2. NoSQL databases (MongoDB 7+, DynamoDB) : data modeling, query optimization, partitioning strategies.

- Proficient in testing methodologies :

1. Unit testing (pytest, unittest), integration testing, mocking (unittest mock, pytest-mock), and Test-Driven Development (TDD) practices.

- Familiarity with Testcontainers for integration tests in containerized environments.

- Containerization and orchestration :

1. Proficiency with Docker (latest best practices, multi-stage builds, slim images).

2. Experience with Kubernetes : deployments, services, config maps, secrets, and Helm charts.

3. Experience guiding and mentoring team members-code reviews, knowledge sharing, and promoting best practices across teams.

- Excellent communication and collaboration skills-able to articulate design choices, trade-offs, and complex technical topics to both technical and non-technical stakeholders.

- Exposure to Machine Learning concepts :

- Familiarity with ML frameworks: TensorFlow 2.x, PyTorch 2.x, scikit-learn, or XGBoost.

- Experience integrating models into production services (model serving, feature stores, API wrappers).

- Hands-on experience with MLOps pipelines and tools

- AWS SageMaker for model training, deployment, and monitoring.

- MLflow, Kubeflow, or ZenML for experiment tracking and reproducibility.

- Bonus : Experience with API design principles (OpenAPI/Swagger) and CI/CD pipelines for Python applications (GitHub Actions, GitLab CI, or Jenkins).

- Experience using Cloud ML platforms and MLOps frameworks in production environments, preferably AWS SageMaker

Desired Experience :

- Background in working on SaaS products

- Experience with AI/ML products

- Enterprise Python Engineer experience

Educational Requirements :

Bachelor's Or Master's degree in one of the following fields :

- Bachelor of Computer Science

- Bachelor of Engineering (Information Technology)

- Masters of Computer Science

- Master of Engineering (Information professional experience in Python Engineer (typically 4+ additional years of hands-on experience beyond the base requirement)

Technical Toolkit :

- Python

- Django

- MongoDB

- Multithreading

- AWS

Why Join DataOrb :

- Mission : Be part of democratizing customer intelligence and making AI accessible

- Impact : Shape how organizations understand and serve their customers

- Team : Work with experienced leaders from top tech companies

- Growth : Rapid scaling environment with significant learning opportunities

- Culture : Autonomous, trust-based environment focused on outcomes

Benefits :

- Flexible work arrangements

- Comprehensive health coverage

- Generous PTO policy

- Professional development support

- Competitive compensation package

Our Values :

- Customer Obsession : We practice what we preach

- Democratizing Technology : Making complex solutions accessible

- Innovation with Purpose : Solving real customer problems

- Trust and Autonomy : Freedom to create and deliver excellence