Data Engineer

Responsibilities

• Design, develop, and maintain high-performance ETL/ELT pipelines using PySpark and Spark SQL
• Build and orchestrate data workflows in Azure
• Implement hybrid data integration between on-premise databases and Azure Databricks using ADF, HVR, or Fivetran
• Optimize Spark jobs for performance, scalability, and cost efficiency
• Implement best practices for data quality, governance, and documentation
• Collaborate with data analysts, scientists, and business users to refine requirements
• Support CI/CD processes and version control using Git
• Perform root cause analysis and troubleshooting for data pipelines

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field
• 2+ years of hands-on experience in data engineering
• Proficiency in PySpark, Spark SQL, and distributed processing
• Strong knowledge of Azure cloud services including ADF, Databricks, and ADLS
• Experience with SQL, data modeling, and performance tuning
• Familiarity with Git, CI/CD pipelines, and agile practices

Preferred Qualifications

• Experience with orchestration tools such as Airflow or ADF pipelines
• Knowledge of real-time streaming tools like Kafka, Event Hub, or HVR
• Exposure to APIs, data integrations, and cloud-native architectures
• Familiarity with enterprise data ecosystems

About the Company

Sandisk understands how people and businesses consume data and relentlessly innovates to deliver solutions that enable today’s needs and tomorrow’s next big ideas through advanced memory technologies.