Staff Data Engineer

Sandisk understands how people and businesses consume data and we relentlessly innovate to deliver solutions that enable today’s needs and tomorrow’s next big ideas. With a rich history of groundbreaking innovations in Flash and advanced memory technologies, our solutions have become the beating heart of the digital world we’re living in and that we have the power to shape.

Sandisk meets people and businesses at the intersection of their aspirations and the moment, enabling them to keep moving and pushing possibility forward. We do this through the balance of our powerhouse manufacturing capabilities and our industry-leading portfolio of products that are recognized globally for innovation, performance and quality.

Sandisk has two facilities recognized by the World Economic Forum as part of the Global Lighthouse Network for advanced 4IR innovations. These facilities were also recognized as Sustainability Lighthouses for breakthroughs in efficient operations. With our global reach, we ensure the global supply chain has access to the Flash memory it needs to keep our world moving forward.

Position Overview

We seek a results-oriented Data Engineer with a minimum of  6+ years of experience in data pipeline development within cloud environments. The successful candidate shall be responsible for designing, constructing, and optimizing Azure-based data ingestion and transformation pipelines using PySpark and Spark SQL. This role requires collaboration with cross-functional teams to deliver high-quality, reliable, and scalable data solutions.

Duties and Responsibilities

• Design, develop, and maintain high-performance ETL/ELT pipelines using PySpark and Spark SQLusing cloud-native components in Databricks
• Build and orchestrate data workflows in AZURE.
• Implement hybrid data integration between on-premise databases and Azure Databricks using tools such as ADF, HVR/Fivetran, and secure network configurations.
• Enhance/optimize Spark jobs for performance, scalability, and cost efficiency.
• Implement and enforce best practices for data quality, governance, and documentation.
• Collaborate with data analysts, data scientists, and business users to define and refine data requirements.
• Support CI/CD processes and automation tools and version control systems like Git.
• Perform root cause analysis, troubleshoot issues, and ensure the reliability of data pipelines.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field.
• 6+ 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.
• Expertise in Databricks, Delta Lake, Unity Catalog and Azure Data Services
• Knowledge of real-time streaming tools (Kafka, Event Hub, HVR).
• Exposure to APIs, data integrations, and cloud-native architectures.
• Familiarity with enterprise data ecosystems (S/4 HANA, BDC)

Sandisk thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We believe the fusion of various perspectives results in the best outcomes for our employees, our company, our customers, and the world around us. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect and contribution.

Sandisk is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and our hiring process. Please contact us at [email protected] to advise us of your accommodation request. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.