
Data Engineer
2 weeks ago
About the Role:
As a Data Engineer, you will design and implement efficient ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines in Databricks using PySpark. Your focus will be on building scalable data architectures that support advanced analytics and machine learning workloads.
Key Responsibilities:
- Collaborate with data analysts and scientists to develop optimized data pipelines
- Design and implement robust data quality checks, error handling, and monitoring mechanisms
- Integrate Databricks with various data sources, including Azure Data Lake, AWS S3, SQL databases, and APIs
- Manage and monitor data pipelines using Databricks Workflows, Job Clusters, and Notebooks
Requirements:
To succeed in this role, you will need strong skills in Python programming, particularly with PySpark. Experience with Databricks, Delta Lake, and Spark is essential. Additionally, you should have a solid understanding of data engineering concepts, including ETL/ELT pipeline design, data warehousing, and big data processing.
Bonus Points:
- Familiarity with cloud-based data platforms and services
- Experience with containerization using Docker or similar technologies
- Knowledge of DevOps practices and tools, such as Jenkins or GitLab CI/CD
We Offer:
A dynamic and collaborative work environment, opportunities for growth and professional development, and a competitive compensation package.
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