Data Platform Engineer: Build Robust Pipelines&Warehousing

Zappos; New York (NY); On-site; USD 111,000 - 185,000; Full time

Benefits offered by this job

RSUs

401(k) matching

Paid time off

Job summary

Zappos is seeking a Data Engineer to design, develop, and maintain its data infrastructure. You will build robust pipelines, optimize ETL processes, and manage data warehousing to support analytics and machine learning initiatives.

The role collaborates with data scientists, analysts, and cross-functional teams, ensuring data quality and security while enabling scalable insights across the organization.

Qualifications

• 1+ years of data engineering experience.
• Experience with data modelling, 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, KornShell).
• Bachelor’s degree.

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 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.
• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and provide the necessary data infrastructure to support their needs.

Skills

Data modelling

Warehousing

ETL pipelines

SQL

Scripting (Python)

Education

Bachelor's degree

Tools

Hadoop

Hive

Spark

EMR

Informatica

Job description

Zappos is seeking a Data Engineer to design, develop, and maintain its data infrastructure. You will build robust pipelines, optimize ETL processes, and manage data warehousing to support analytics and machine learning initiatives.

The role collaborates with data scientists, analysts, and cross-functional teams, ensuring data quality and security while enabling scalable insights across the organization.