Job Description

Introduction: Are you an innovative thinker with a passion for designing and implementing scalable processes? Our client is looking for an Engineer responsible for creating robust data processes, developing a data platform, implementing cloud solutions, supporting DevOps practices, applying machine learning techniques, and creating data visualizations.Minimum Requirements:

• Education Background:

• Bachelors degree in Computer Science, Business Informatics, Mathematics, Statistics, Engineering, or a related field.
• Professional Experience:

• 4-5 years of relevant engineering experience.

• Strong understanding of data structures, algorithms, and software design.
• Experience with structured and unstructured data, different data stores, and traditional RDBMS and data warehouses.
• Proficiency in programming languages such as Python, Scala, Java, and C.
• Practical experience with Apache Spark and cloud services (e.g., AWS, Azure, GCP).
• Experience with version control systems like Git and SVN.
• Proficiency in data visualization tools such as PowerBI, QuickSight, and QlikSense.
• Experience with DevOps practices, including CI/CD and Infrastructure as Code.

• Data Engineering:

• Experience with big data, ETL, and data management processes.
• Platform Engineering:

• Experience with cloud platform development and maintenance.
• Cloud Engineering:

• Experience with cloud architecture and API development.
• DevOps:

• Experience with DevOps practices, architecture, and operation.
• ML Engineering:

• Experience with machine learning frameworks and model integration.
• Data Visualization:

• Experience with data visualization tools and techniques.

• Data Process Design and Implementation:

• Design and implement scalable processes for ingesting and transforming large data sets.
• Develop and maintain data platforms and deploy cloud solutions.

• Data Pipeline Management:

• Design, implement, and maintain data pipelines for large, complex data sets from various sources
• Cloud Strategy Implementation:

• Align cloud strategies with data architecture, security, and governance requirements.
• DevOps Practices:

• Enhance efficiency and automation through DevOps practices, including infrastructure and solutions as code.

• Machine Learning Integration:

• Integrate machine learning models into data processes and the ML platform.
• Data Visualization

• Design processes for visualizing large data sets, enabling self-service visualization and analytics.
• API Development:

• Develop APIs to expose data to enterprise applications and third-party vendors.
• Stakeholder Collaboration:

• Work with various stakeholders to understand data requirements and apply technical knowledge to solve key business problems.
• Operational Support:

• Provide support in the operational environment with all relevant support teams for data services.

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