Big Data Engineer I

About SHEIN 

SHEIN is a global online fashion and lifestyle retailer, offering SHEIN branded apparel and products from a global network of vendors, all at affordable prices. Headquartered in Singapore, with more than 15,000 employees operating from offices around the world, SHEIN is committed to making the beauty of fashion accessible to all, promoting its industry-leading, on-demand production methodology, for a smarter, future-ready industry. 

Position Summary 

SHEIN Technology LLC is seeking a full-time Big Data Engineer I to join our San Diego-based corporate office located at 3111 Camino Del Rio N, Suite 1300, San Diego, CA 92108. The Big Data team has the unique opportunity to own the US-based Big Data operations, refactor and redesign, while partnering with an established global organization. We are looking for an experienced Big Data Engineer to support operations, solve complex issues, and drive growth. We achieve this goal by building and deploying highly scalable data pipelines, adhering to software/data engineering best practices, and ensuring the security and quality of our data to the delight of our consumers.

Job Description:

Build and deploy highly scalable data pipelines to move and transform data and ensure the security and quality of data. Optimize and maintain all domain-related data pipelines. Partner in a cross-functional global organization across data, security, infrastructure, and business teams to understand data needs. Implement inclusive data quality checks to ensure high quality of data. Implement and enforce data security policies and ensure compliance with relevant regulations and standards. Provide 24x7 on-call support on a rotational basis.

Minimum Education & Experience Requirements:

Master’s degree or a foreign equivalent in Applied Data Science, Computer Science, or a related field, plus 1 year of post-baccalaureate experience in job offered or any related job titles. Applicant must possess at least 1 year of experience in the following:

1. Building and optimizing large-scale, distributed data pipelines with Hive, Presto, Spark, or Flink.
2. Data warehousing, including dimensional modeling, star/snowflake schema design, and normalization/denormalization strategies in large-scale data warehouses including Amazon Redshift.
3. Writing and optimizing complex SQL queries for large datasets, creating joins, aggregations, and subqueries, in the context of querying data warehouses.
4. Data storage solutions, including S3 on AWS.
5. Cloud-native services including AWS EMR, AWS S3.
6. Using workflow orchestration tools including Airflow in a production environment to automate, schedule, monitor and tune, data pipelines.