Data Engineer , Zappos Analytics

As a Data Engineer at Zappos, you will play a crucial role in designing, developing, and maintaining our data infrastructure. You will work closely with cross-functional teams to ensure that data is collected, processed, and made available for analysis, reporting, and machine learning applications. Your expertise in data pipelines, ETL processes, and data warehousing will be instrumental in shaping our data ecosystem.

The right candidate will be excited by the opportunity to redesign our company’s data architecture to support our next generation of data initiatives.

• Design, build, and maintain robust data pipelines to acquire, process, and store data from various sources such as databases, APIs, and external data providers.
• Develop and optimise ETL (Extract, Transform, Load) processes to clean, enrich, and structure raw data into a usable format for analysis and reporting.
• Implement and manage data warehousing solutions to ensure efficient data storage, retrieval, and query performance.
• Establish data quality standards, perform data validation, and proactively identify and address data quality issues.
• Optimize data pipelines and storage solutions to handle large volumes of data while maintaining high performance and reliability.
• Ensure data privacy and security by implementing access controls, encryption, and compliance with data protection regulations.
• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and provide the necessary data infrastructure to support their needs.
• Maintain comprehensive documentation for data pipelines, data models, and processes to facilitate knowledge sharing and troubleshooting.
• Implement monitoring solutions to proactively detect and address data pipeline failures or performance bottlenecks.
• Keep abreast of industry trends and emerging technologies in data engineering to recommend and implement improvements to our data infrastructure.

• Design, build, and maintain robust data pipelines to acquire, process, and store data from various sources such as databases, APIs, and external data providers.
• Develop and optimise ETL (Extract, Transform, Load) processes to clean, enrich, and structure raw data into a usable format for analysis and reporting.
• Implement and manage data warehousing solutions to ensure efficient data storage, retrieval, and query performance.
• Establish data quality standards, perform data validation, and proactively identify and address data quality issues.
• Optimize data pipelines and storage solutions to handle large volumes of data while maintaining high performance and reliability.

The Zappos Analytics team transforms data into actionable insights, empowering business partners to make data-driven decisions that drive profitability and growth. We develop performance metrics and visualisations using various data sources across the organisation. Working with AWS technologies, you'll collaborate with cross-functional teams to solve challenging business problems and help stakeholders gain valuable insights quickly and effectively.

• 1+ years of data engineering experience
• Experience with data modeling, warehousing and building ETL pipelines
• Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
• Experience with one or more scripting language (e.g., Python, Korn Shell)
• Bachelor's degree

• Experience with big data technologies such as:
  Hadoop, Hive, Spark, EMR
• Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
• Usage of generative AI tools to enhance workflow efficiency, with a willingness to learn effective prompting and evaluation practices.
• Ability to…