Principal Engineer, Software Development Engineewring(Apps)

Company Description

At Western Digital, our vision is to power global innovation and push the boundaries of technology to make what you thought was once impossible, possible.

At our core, Western Digital is a company of problem solvers. People achieve extraordinary things given the right technology. For decades, we’ve been doing just that. Our technology helped people put a man on the moon.

We are a key partner to some of the largest and highest growth organizations in the world. From energizing the most competitive gaming platforms, to enabling systems to make cities safer and cars smarter and more connected, to powering the data centers behind many of the world’s biggest companies and public cloud, Western Digital is fueling a brighter, smarter future.

Binge-watch any shows, use social media or shop online lately? You’ll find Western Digital supporting the storage infrastructure behind many of these platforms. And, that flash memory card that captures and preserves your most precious moments? That’s us, too.

We offer an expansive portfolio of technologies, storage devices and platforms for business and consumers alike. Our data-centric solutions are comprised of the Western Digital®, G-Technology™, SanDisk® and WD® brands.

Today’s exceptional challenges require your unique skills. It’s You & Western Digital. Together, we’re the next BIG thing in data.

Job Description

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.

Qualifications

• Education: Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, or a related field.
• Experience: Minimum of 7-10 years of experience in software development and data science, with a strong emphasis on .NET Core and Python.
• Technical Skills:
  • Proficiency in .NET Core and Python.
  • Strong experience with data science libraries and frameworks (e.g., GCP, BigData, ).
  • Experience with SQL and NoSQL databases.
  • Familiarity with RESTful APIs and microservices architecture.
  • Knowledge of front-end technologies (e.g., HTML, CSS, JavaScript) is a plus.
• Analytical Skills: Strong analytical and problem-solving skills, with the ability to analyze complex data sets and derive actionable insights.
• Communication Skills: Excellent written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• Team Player: Proven ability to work effectively in a collaborative team environment.
• Adaptability: Ability to adapt to changing business requirements and work in a fast-paced environment.

Preferred Qualifications:

• Certifications: Relevant certifications in .NET Core, Python, or Data Science.
• Cloud Experience: Experience with cloud platforms such as Azure, AWS, or Google Cloud.
• Machine Learning: Hands-on experience with machine learning algorithms and frameworks.
• DevOps: Familiarity with DevOps practices and tools (e.g., Docker, Kubernetes, CI/CD pipelines).

Additional Information

All your information will be kept confidential according to EEO guidelines.