
Visionary Data Engineer Leader
2 days ago
Data Engineering Role
Our data engineering team is seeking a seasoned and proficient Senior Data Engineer with substantial experience in cloud technologies to play a crucial role in designing, implementing, and optimizing data pipelines.
">Data Pipelines Development:
- Design and develop scalable and efficient data pipelines using Python and cloud-based technologies.
- Implement Extract, Transform, Load (ETL) processes to move data from diverse sources into our cloud-based data warehouse.
Cloud Integration:
- Utilize cloud platforms (e.g., Google Cloud, AWS, Azure) to deploy, manage, and optimize data engineering solutions.
- Leverage cloud-native services for storage, processing, and analysis of large datasets.
Data Modelling and Architecture:
- Collaborate with data scientists, analysts, and other stakeholders to design effective data models that align with business requirements.
- Ensure the scalability, reliability, and performance of the overall data infrastructure on cloud platforms.
Optimization and Performance:
- Continuously optimize data processes for improved performance, scalability, and cost-effectiveness in a cloud environment.
- Monitor and troubleshoot issues, ensuring timely resolution and minimal impact on data availability.
Quality Assurance:
- Implement data quality checks and validation processes to ensure the accuracy and completeness of data in the cloud-based data warehouse.
Collaboration and Communication:
- Work closely with data scientists, analysts, and other teams to understand data requirements and provide technical support.
Technical Skills:
- Programming Languages: Proficiency in Python for data engineering tasks, scripting, and automation.
- Cloud Platforms: In-depth knowledge and hands-on experience with at least one major cloud platform: AWS, Azure, or Google Cloud.
- Familiarity with cloud-native services for data processing, storage, and analytics.
- Containerization and Orchestration: Familiarity with containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes).
- Monitoring and Troubleshooting: Ability to implement monitoring solutions and troubleshoot issues in data pipelines.
- Agile Methodologies: Familiarity with agile development methodologies and practices.
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