Data Engineer I, Zappos Analytics

Data Engineer

As a Data Engineer at Zappos, you will design, develop, and maintain 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 help shape 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.

Key Responsibilities

• 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 optimize ETL (Extract, Transform, Load) processes to clean, enrich, and structure raw data into usable formats 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.
• Maintain comprehensive documentation for data pipelines, data models, and processes to support knowledge sharing and troubleshooting.
• Implement monitoring solutions to proactively detect and address data pipeline failures or performance bottlenecks.
• Stay current on industry trends and emerging data engineering technologies to recommend and implement improvements to our data infrastructure.

A Day in the Life

• 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 optimize ETL (Extract, Transform, Load) processes to clean, enrich, and structure raw data into usable formats 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.

About the Team

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 visualizations using various data sources across the organization. Working with AWS technologies, you will collaborate with cross-functional teams to solve challenging business problems and help stakeholders gain valuable insights quickly and effectively.

Basic Qualifications

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

Preferred Qualifications

• Experience with big data technologies such as Hadoop, Hive, Spark, and EMR
• Experience with one or more ETL tools such as 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 recognize opportunities where generative AI could enhance products, workflows, or customer experiences

Location & Compensation

USA, NY, New York - 111,400.00 - 185,000.00 USD annually