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.

Technical Skills:

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.

Specialized Skills:

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.

Job Description:

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.