
Mastering Large-Scale Data Engineering
7 days ago
The ideal candidate will have 3-5 years of experience in data engineering and a strong background in distributed data processing and storage.
Key Responsibilities:
- Design, develop, and maintain large-scale data pipelines using Python, SQL, and cloud-based technologies.
- Collaborate with cross-functional teams to integrate data into various systems and applications.
- Develop and implement data quality checks and metrics to ensure high-quality data.
- Stay up-to-date with industry trends and emerging technologies in data engineering.
Nice to Have:
- Experience with Azure Databricks.
- Knowledge of DevOps - ETL pipeline orchestration using tools like GitHub actions, Terraform, Databricks workflows, and Azure DevOps.
- Certifications in Databricks, Azure, AWS, or GCP.
Requirements:
- Proficient in Python, including popular packages like Pandas and NumPy.
- Strong background in distributed data processing and storage, including Apache, Spark, and Hadoop.
- Large scale (TBs of data) data engineering skills, including model data and creating production-ready ETL pipelines.
- Development experience with at least one cloud platform, preferably Azure, AWS, or GCP.
- Knowledge of data lake and data lake house patterns.
- Knowledge of ETL performance tuning and cost optimization.
- Knowledge of data structures and algorithms and good software engineering practices.
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