We are seeking a highly skilled and motivated Principal Engineer with expertise in . NET Core and Python for data science to join our dynamic team. The ideal candidate will possess a strong foundation in software development and data analysis, capable of leveraging these skills to deliver innovative solutions and insights. As a senior developer, you will be instrumental in developing and optimizing our data-driven applications and systems.
Key Responsibilities:
- Develop and Maintain Applications : Design, develop, and maintain robust applications using . NET Core and Python.
- Data Analysis and Modeling : Perform data analysis, create predictive models, and develop data-driven solutions to support business objectives.
- Integration : Integrate data from various back-end services and databases, ensuring seamless data flow across systems.
- Collaboration : Work closely with cross-functional teams, including data engineers, data scientists, and business stakeholders, to gather requirements and deliver solutions.
- Performance Optimization : Optimize applications for maximum speed and scalability.
- Code Quality : Write clean, scalable, and maintainable code, ensuring adherence to best practices and industry standards.
- Research and Development : Stay up-to-date with the latest industry trends and technologies in . NET Core, Python, and data science, and apply this knowledge to improve existing processes and systems.
Tech Stack:
- . NET Core :
- ASP. NET Core : For building web applications and APIs. (. NET Core MVC, Ado. Net, C#, WCF, Web Services, Windows, )Services, SSIS, MSMQ, Web Sockets
- Entity Framework Core : For ORM (Object-Relational Mapping) and data access or Dapper.
- SignalR : For real-time web functionalities.
- IdentityServer : For authentication and authorization.
- Python :
- Open Telemetry Stack
- Pandas : For data manipulation and analysis.
- NumPy : For numerical computations.
- Scikit-Learn : For machine learning algorithms.
- TensorFlow/Keras : For deep learning and neural networks.
- Matplotlib/Seaborn : For data visualization.
- Jupyter Notebooks : For interactive data exploration and visualization.
- Databases :
- SQL Databases : Such as SQL Server, PostgreSQL, or MySQL for relational data management.
- NoSQL Databases : Such as MongoDB or Cassandra for non-relational data management.
- Data Warehouses : Such as Azure Synapse Analytics, AWS Redshift, or Google BigQuery.
- Cloud Platforms (Any one) :
- Microsoft Azure : For cloud services, including Azure App Services, Azure Functions, Azure SQL Database, and Azure Machine Learning.
- AWS : For cloud services, including AWS Lambda, Amazon RDS, and Amazon SageMaker.
- Google Cloud Platform (GCP) : For cloud services, including Google App Engine, Anthos, Cloud Functions, and BigQuery.
- DevOps :
- CI/CD Tools : Such as Azure DevOps or Jenkins or GitHub Actions or GitLab CI.
- Containerization : Using Docker to create and manage containers.
- Orchestration : Using Kubernetes for container orchestration.
- Version Control : Using Git for source code management.
- Front-End Technologies (Preferred):
- JavaScript Frameworks : Such as React, Angular, or Vue. js.
- HTML/CSS : For web development.
- APIs :
- RESTful APIs : For standard API development.
- GraphQL : For flexible API queries.
