
Chief Data Processing Specialist
1 week ago
Key Challenges and Opportunities
The Senior Data Engineer will be responsible for designing, developing, and maintaining large-scale data processing workflows using Azure Databricks, PySpark, and Apache Spark.
Responsibilities:
- Design cloud-based data architectures, including data lakes, lakehouses, and warehouses, following best practices.
- Deploy and optimize data solutions on Azure (preferred), AWS, or GCP with a focus on performance, security, and scalability.
- Develop and optimize ETL/ELT pipelines for structured and unstructured data from various sources.
- Automate data workflows using CI/CD and DevOps best practices, ensuring security and compliance with industry standards.
- Monitor, troubleshoot, and enhance data pipelines for high availability and reliability.
- Collaborate with cross-functional teams to provide clean, structured data for analytics and AI/ML models.
Requirements:
- 4+ years of experience in data engineering.
- Proficient in Azure Databricks, PySpark, and Apache Spark.
- Experience with data pre-processing, including cleaning, transformation, deduplication, normalization, encoding, and scaling.
- Knowledge of cloud-based data architectures, including data lakes, lakehouses, and warehouses.
- Experience with ETL/ELT pipelines and automation tools like CI/CD and DevOps.
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